Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges

Bohao Wang; Chenxiao Fan; Chongming Gao; Fuli Feng; Jiawei Chen; Jizhi Zhang; Jujia Zhao; Shengjia Zhang; Sirui Chen; Weiqin Yang

REVIEW 2 major objections 300 references

Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges

T0 review · 2 major / 0 minor · reviewed 2026-06-28 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Large language models act as a double-edged sword for trustworthy recommendation systems by enabling new capabilities while creating fresh risks.

desk verdict This survey organizes the LLM-trustworthy recsys space into a usable taxonomy of 13 opportunities and 18 challenges, but the missing search protocol leaves the completeness claim open to bias. read the letter →

arxiv 2606.00540 v1 pith:LJL6ZJZU submitted 2026-05-30 cs.IR

Bohao Wang , Yu Cui , Zhenxiang Xu , Jujia Zhao , Chenxiao Fan , Jizhi Zhang , Weiqin Yang , Shengjia Zhang

show 8 more authors

Sirui Chen Yang Zhang Xiaoyan Zhao Wenjie Wang Chongming Gao Fuli Feng Xiangnan He Jiawei Chen

This is my paper · ORCID

classification cs.IR

keywords recommendersystemslargelanguagemodelstrustworthinessfairnessrobustnessprivacybiashallucinations

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Recommender systems have shifted focus from raw accuracy toward trustworthiness dimensions such as robustness, fairness, and privacy. Large language models are now widely integrated into these systems, supplying richer semantic understanding and flexible interactions. A review of more than 200 studies concludes that this integration produces both gains and drawbacks: LLMs can strengthen trustworthiness through advanced reasoning yet also inject novel biases and hallucination problems. The work organizes the literature into a taxonomy of 13 opportunities and 18 challenges spread across six core trustworthiness dimensions. It further surveys datasets, metrics, and remaining open questions to guide empirical work.

What carries the argument

A taxonomy classifying existing literature on LLM-empowered recommendation into 13 opportunities and 18 challenges distributed across six trustworthiness dimensions.

What would settle it

Discovery of a sizable set of studies on LLM-based recommendation whose findings fall outside the proposed 13 opportunities and 18 challenges or that reveal major uncovered areas in the six dimensions.

Watch

Extended reading notes

Core claim

The integration of large language models into recommender systems produces a dual effect on trustworthiness. While the models supply mechanisms for stronger intent reasoning and user interaction that can improve robustness, fairness, and privacy, they simultaneously introduce new failure modes including amplified bias and hallucination-driven errors. Systematic analysis of over 200 studies yields a taxonomy that identifies 13 opportunities and 18 challenges across six fundamental dimensions, together with a survey of supporting datasets and evaluation metrics.

Load-bearing premise

A comprehensive review of over 200 studies produces an unbiased and gap-free taxonomy of all relevant opportunities and challenges.

Editorial extensions

If this is right

Evaluation protocols must incorporate new metrics that detect LLM-specific issues such as hallucination in generated recommendations.
Mitigation strategies are required to address novel bias forms introduced by language-model reasoning.
Privacy techniques need adaptation to handle the semantic inference capabilities of LLMs.
Fairness definitions should be revised to account for biases that arise from LLM training data and generation processes.
Future systems will need hybrid designs that selectively apply LLM components only where their benefits exceed the added risks.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The taxonomy may need extension once multimodal large language models become common in recommendation pipelines.
High-stakes applications such as medical or financial recommendations may require extra verification layers not yet captured in the current challenge list.
Transferable techniques from general large-model safety research could address some of the hallucination and bias challenges identified here.
Large-scale experiments on the reviewed datasets could quantify whether the net effect of LLMs on trustworthiness is positive or negative in practice.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Desk Editor's Note

read the letter

The core takeaway is that LLMs create both gains and new problems for trustworthiness in recommendation. The authors map this across six dimensions and count 13 opportunities against 18 challenges from their reading of over 200 papers.

What the paper actually does is build a taxonomy that separates the literature by how LLMs affect each trustworthiness aspect. That structure is new enough to be useful for someone trying to place their own work. They also pull together datasets and metrics in one place, which saves time for anyone planning experiments.

The main weakness is the lack of any documented review method. The abstract says they analyzed over 200 studies but gives no databases, keywords, date ranges, or screening rules. Without that, it is hard to know whether the 13/18 split reflects the field or just the papers the authors happened to find. The central claims rest on that synthesis, so the gap matters.

This is a survey, not a new empirical result or derivation. It will mainly help researchers already in information retrieval or AI ethics who want an entry point into the LLM angle. A reader outside that subfield will find less immediate use.

The work deserves peer review because the topic is timely and the taxonomy could reduce duplication. Reviewers should focus on whether the opportunity-challenge counts hold once the selection process is shown. I would send it out rather than desk reject.

Referee Report

2 major / 0 minor

Summary. The paper presents a systematic review of trustworthy LLM-empowered recommender systems. It claims that integrating LLMs into RS creates a double-edged effect: 13 opportunities to improve trustworthiness (across dimensions such as robustness, fairness, and privacy) alongside 18 new challenges (including novel biases and hallucination issues). The authors synthesize findings from over 200 studies into a novel taxonomy, review datasets and evaluation metrics, and outline future research directions.

Significance. A well-executed synthesis of this scope could provide a useful organizing framework for an emerging intersection of LLMs and trustworthy RS, helping researchers navigate both enhancements and risks. The explicit enumeration of opportunities and challenges, together with the dataset/metric review, would be particularly valuable if the underlying corpus selection is reproducible.

major comments (2)

[Abstract] Abstract: The central claim—that a comprehensive analysis of >200 studies yields a complete taxonomy of 13 opportunities and 18 challenges across six trustworthiness dimensions—rests on an undocumented literature review process. No search strategy, databases, keyword strings, date cutoffs, inclusion/exclusion criteria, or inter-rater reliability measures are described, leaving the taxonomy vulnerable to selection bias and undermining reproducibility of the opportunity/challenge mapping.
[Abstract] The headline characterization of LLMs as a 'double-edged sword' is derived entirely from the synthesis; without the missing methodological details, it is impossible to assess whether counter-examples or underrepresented venues were systematically omitted, which directly affects the load-bearing claim that the taxonomy is representative.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for highlighting the need for greater transparency in our literature review process. We agree that explicit methodological details are required to support the reproducibility and representativeness claims in a systematic review. We address both comments below and will revise the manuscript accordingly.

read point-by-point responses

Referee: [Abstract] Abstract: The central claim—that a comprehensive analysis of >200 studies yields a complete taxonomy of 13 opportunities and 18 challenges across six trustworthiness dimensions—rests on an undocumented literature review process. No search strategy, databases, keyword strings, date cutoffs, inclusion/exclusion criteria, or inter-rater reliability measures are described, leaving the taxonomy vulnerable to selection bias and undermining reproducibility of the opportunity/challenge mapping.

Authors: We agree that the current manuscript lacks a dedicated description of the literature search and selection process. This omission weakens the ability to evaluate selection bias and reproducibility. In the revision we will add a new 'Review Methodology' subsection (likely in Section 2 or as an appendix) that explicitly documents: (1) the databases and repositories searched (Google Scholar, arXiv, ACM DL, IEEE Xplore), (2) the keyword strings and Boolean combinations used, (3) the time window (primarily 2022 onward for LLM-related work), (4) inclusion/exclusion criteria, (5) the screening and coding procedure, and (6) any inter-rater checks performed. This will allow readers to assess how the 13 opportunities and 18 challenges were mapped from the corpus. revision: yes
Referee: [Abstract] The headline characterization of LLMs as a 'double-edged sword' is derived entirely from the synthesis; without the missing methodological details, it is impossible to assess whether counter-examples or underrepresented venues were systematically omitted, which directly affects the load-bearing claim that the taxonomy is representative.

Authors: The double-edged-sword framing is an interpretive summary of the opportunities and challenges identified across the reviewed papers rather than an independent empirical claim. Nevertheless, we accept that without documented search and selection procedures it is difficult to judge coverage or the risk of omitted counter-examples. The added methodology section will include a limitations paragraph that discusses venue coverage, the recency bias inherent to LLM literature, and any steps taken to mitigate under-representation. We will also make the full list of reviewed papers available as supplementary material to further support scrutiny of the taxonomy. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

Survey synthesis draws from external literature; no self-referential derivations or load-bearing self-citations

full rationale

The paper is a literature review that organizes >200 external studies into a taxonomy of 13 opportunities and 18 challenges. No equations, fitted parameters, predictions, or uniqueness theorems are present. The central claim (LLMs as double-edged sword) is framed as emerging from analysis of cited external work rather than reducing to any input by construction or author-overlapping citation chain. Self-citation, if present, is not load-bearing for the taxonomy itself. This meets the default expectation of no significant circularity.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

As a literature survey, the paper introduces no new free parameters, mathematical axioms, or invented entities; it relies on standard practices for systematic reviews.

assumptions (1)

domain assumption Standard practices for conducting systematic literature reviews are followed when selecting and analyzing the 200 studies.
The abstract claims a comprehensive analysis without detailing deviations from established review protocols.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges." pith.science (2026). https://pith.science/paper/LJL6ZJZU

@misc{pith2026260600540,
  author       = {Pith},
  title        = {Pith review of: Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LJL6ZJZU}},
  note         = {Machine review of arXiv:2606.00540}
}

read the original abstract

The field of recommender systems (RS) is currently undergoing two profound paradigm shifts. From the perspective of objectives, the goal has shifted beyond mere recommendation accuracy to comprehensive trustworthiness, encompassing multiple dimensions such as robustness, fairness, and privacy preservation. From a technical perspective, Large Language Models (LLMs) have been extensively integrated into RS, reshaping the foundations of recommendation through richer semantic understanding, stronger intent reasoning, and more flexible user interactions. The convergence of these two shifts prompts a timely and pivotal question: how does the integration of LLMs reshape the landscape of trustworthy recommendation? In this work, we present a systematic review of trustworthy LLM-empowered recommendation. By comprehensively analyzing over 200 recent studies, we reveal that the introduction of LLMs acts as a double-edged sword. While their advanced mechanisms and user-friendly interfaces offer unprecedented opportunities to enhance trustworthiness, they simultaneously introduce new risks, such as novel forms of bias and hallucination-induced issues. To characterize this dual impact, we systematically identify 13 opportunities and 18 challenges across six fundamental dimensions of trustworthiness, and accordingly organize the existing literature into a novel taxonomy. We also provide a comprehensive review of commonly used datasets and evaluation metrics to facilitate empirical validation. Finally, we identify critical open challenges and outline future directions, hoping to inspire future research on this emerging topic.

Figures

Figures reproduced from arXiv: 2606.00540 by the authors.

**Figure 1.** Overview of this survey structure. recommendation. Therefore, we believe it is both timely and imperative to present a comprehensive survey of LLMempowered trustworthy recommendation. Contributions. In this work, we provide a systematic review of this growing research area, focusing primarily on how the integration of LLMs changes the landscape of trustworthy recommendation. By comprehensively analyzing over 200 wo… view at source ↗

**Figure 2.** Illustration of the paradigms of traditional recommender systems and LLM-empowered recommenders. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Six key dimensions of trustworthy recommendation in the era of large language models. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (7 more)

**Figure 4.** Figure 4: Opportunities and challenges of LLM-empowered recommendation across six dimensions of trustworthiness. Red boxes denote [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 5.** Figure 5: An illustration of the opportunities and challenges introduced by LLMs for the robustness of recommender systems. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

**Figure 6.** Figure 6: An illustration of the opportunities and challenges introduced by LLMs for the bias and fairness of recommender systems. [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]

**Figure 7.** Figure 7: An illustration of the opportunities and challenges introduced by LLMs for the explainability of recommender systems. [PITH_FULL_IMAGE:figures/full_fig_p015_7.png]

**Figure 8.** Figure 8: An illustration of the challenges introduced by LLMs for the factuality of recommender systems. [PITH_FULL_IMAGE:figures/full_fig_p018_8.png]

**Figure 9.** Figure 9: An illustration of the opportunities and challenges introduced by LLMs for the controllability of recommender systems. [PITH_FULL_IMAGE:figures/full_fig_p020_9.png]

**Figure 10.** Figure 10: An illustration of the opportunities and challenges introduced by LLMs for the privacy of recommender systems. [PITH_FULL_IMAGE:figures/full_fig_p023_10.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

300 extracted references · 92 canonical work pages

[1]

Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, and Luiz Pizzato
[2]

Abdollahpouri et al.User Modeling and User-Adapted Interaction30, 1 (2020), 127–158

Multistakeholder recommendation: Survey and research directions: H. Abdollahpouri et al.User Modeling and User-Adapted Interaction30, 1 (2020), 127–158

2020
[3]

Hasan Abu-Rasheed, Christian Weber, and Madjid Fathi. 2024. Knowledge graphs as context sources for llm-based explanations of learning recommendations. In2024 IEEE Global Engineering Education Conference (EDUCON). IEEE, 1–5

2024
[4]

Aaron Adcock, Aayushi Srivastava, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pande, Abhinav Pandey, Abhinav Sharma, Abhishek Kadian, Abhishek Kumawat, Adam Kelsey, et al. 2026. The Llama 4 Herd: Architecture, Training, Evaluation, and Deployment Notes.arXiv preprint arXiv:2601.11659(2026)

work page arXiv 2026
[5]

Julien Albert, Martin Balfroid, Miriam Doh, Jérémie Bogaert, Luca La Fisca, Liesbet De Vos, Bryan Renard, Vincent Stragier, and Emmanuel Jean. 2024. User Preferences for Large Language Model versus Template-Based Explanations of Movie Recommendations: A Pilot Study.ArXiv abs/2409.06297 (2024). https://api.semanticscholar.org/CorpusID:272550316

work page arXiv 2024
[6]

Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz. 2019. Invariant risk minimization.arXiv preprint arXiv:1907.02893(2019)

work page Pith review arXiv 2019
[7]

Giacomo Balloccu, Ludovico Boratto, Christian Cancedda, Gianni Fenu, and Mirko Marras. 2023. Faithful Path Language Modeling for Explainable Recommendation over Knowledge Graph.arXiv preprint arXiv:2310.16452(2023)

work page arXiv 2023
[8]

Yejin Bang, Ziwei Ji, Alan Schelten, Anthony Hartshorn, Tara Fowler, Cheng Zhang, Nicola Cancedda, and Pascale Fung. 2025. Hallulens: Llm hallucination benchmark. InProceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 24128–24156

2025

Show all 300 references

[9]

Keqin Bao, Jizhi Zhang, Wenjie Wang, Yang Zhang, Zhengyi Yang, Yanchen Luo, Chong Chen, Fuli Feng, and Qi Tian. 2025. A bi-step grounding paradigm for large language models in recommendation systems.ACM Transactions on Recommender Systems3, 4 (2025), 1–27

2025
[10]

Keqin Bao, Jizhi Zhang, Yang Zhang, Xinyue Huo, Chong Chen, and Fuli Feng. 2024. Decoding matters: Addressing amplification bias and homogeneity issue in recommendations for large language models. InProceedings of the 2024 Conference on Empirical Methods in Natural Language Pr...

2024
[11]

Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023. Tallrec: An effective and efficient tuning framework to align large language model with recommendation. InProceedings of the 17th ACM Conference on Recommender Systems. 1007–1014

2023
[12]

Milena Belosevic. 2025. User-Centric Design Paradigms for Trust and Control in Human-LLM-Interactions: A Survey. InProceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+ NLP). 17–32

2025
[13]

Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira. 2006. Analysis of representations for domain adaptation.Advances in neural information processing systems19 (2006)

2006
[14]

Millennium Bismay, Xiangjue Dong, and James Caverlee. 2024. ReasoningRec: Bridging Personalized Recommendations and Human-Interpretable Explanations through LLM Reasoning.arXiv preprint arXiv:2410.23180(2024)

2024
[15]

Ethan Bito, Yongli Ren, and Estrid He. 2025. Evaluating Position Bias in Large Language Model Recommendations.arXiv preprint arXiv:2508.02020 (2025)

2025
[16]

Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramer, and Chiyuan Zhang. 2022. Quantifying memorization across neural language models. InThe Eleventh International Conference on Learning Representations

2022
[17]

Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al. 2021. Extracting training data from large language models. In30th USENIX security symposium (USENIX Security 21). ...

2021
[18]

Micah Carroll, Adeline Foote, Marcus Williams, Anca Dragan, W Bradley Knox, and Smitha Milli. [n. d.]. CTRL-Rec: Controlling Recommender Systems With Natural Language. InICLR 2025 Workshop on Bidirectional Human-AI Alignment

2025
[19]

Yukuo Cen, Jianwei Zhang, Xu Zou, Chang Zhou, Hongxia Yang, and Jie Tang. 2020. Controllable multi-interest framework for recommendation. InProceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. 2942–2951

2020
[20]

Allison JB Chaney, Brandon M Stewart, and Barbara E Engelhardt. 2018. How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. InProceedings of the 12th ACM conference on recommender systems. 224–232

2018
[21]

Ting-Jui Chang, Lydia Hsiao-Mei Lin, and Richard Tzong-Han Tsai. 2024. Conversational product recommendation using LLM. In2024 IEEE 4th International Conference on Electronic Communications, Internet of Things and Big Data (ICEIB). IEEE, 340–343. Manuscript submitted to ACM 36...

2024
[22]

Shangkun Che, Minjia Mao, and Hongyan Liu. 2024. New Community Cold-Start Recommendation: A Novel Large Language Model-based Method. (2024)

2024
[23]

Chong Chen, Fei Sun, Min Zhang, and Bolin Ding. 2022. Recommendation unlearning. InProceedings of the ACM web conference 2022. 2768–2777

2022
[24]

Hanxiong Chen, Shaoyun Shi, Yunqi Li, and Yongfeng Zhang. 2021. Neural collaborative reasoning. InProceedings of the web conference 2021. 1516–1527

2021
[25]

Junyi Chen, Lu Chi, Bingyue Peng, and Zehuan Yuan. 2024. Hllm: Enhancing sequential recommendations via hierarchical large language models for item and user modeling.arXiv preprint arXiv:2409.12740(2024)

2024
[26]

Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2023. Bias and debias in recommender system: A survey and future directions.ACM Transactions on Information Systems41, 3 (2023), 1–39

2023
[27]

Jiaju Chen, Chongming Gao, Shuai Yuan, Shuchang Liu, Qingpeng Cai, and Peng Jiang. 2025. DLCRec: A Novel Approach for Managing Diversity in LLM-Based Recommender Systems. InProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining. 857–865

2025
[28]

Wei Chen and Dexin Chen. 2025. LLM-as-Critic: Contrastive and Adversarial Strategies for Authentic Text Verification. (2025)

2025
[29]

Weixin Chen, Yuhan Zhao, Jingyuan Huang, Zihe Ye, Clark Mingxuan Ju, Tong Zhao, Neil Shah, Li Chen, and Yongfeng Zhang. 2026. MemRec: Collaborative Memory-Augmented Agentic Recommender System.arXiv preprint arXiv:2601.08816(2026)

2026 arXiv
[30]

Hung-Yun Chiang, Yi-Syuan Chen, Yun-Zhu Song, Hong-Han Shuai, and Jason S Chang. 2023. Shilling black-box review-based recommender systems through fake review generation. InProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 286–297

2023
[31]

Chen-Yao Chung, Ping-Yu Hsu, and Shih-Hsiang Huang. 2013. 𝛽P: A novel approach to filter out malicious rating profiles from recommender systems.Decision Support Systems55, 1 (2013), 314–325

2013
[32]

Yu Cui, Feng Liu, Pengbo Wang, Bohao Wang, Heng Tang, Yi Wan, Jun Wang, and Jiawei Chen. 2024. Distillation matters: empowering sequential recommenders to match the performance of large language models. InProceedings of the 18th ACM Conference on Recommender Systems. 507–517

2024
[33]

Zeyu Cui, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang. 2022. M6-rec: Generative pretrained language models are open-ended recommender systems.arXiv preprint arXiv:2205.08084(2022)

2022
[34]

Ivens da Silva Portugal, Paulo Alencar, and Donald Cowan. 2026. Agentic Recommender Systems: A Systematic Literature Review.IEEE Transactions on Software Engineering(2026)

2026
[35]

Sunhao Dai, Chen Xu, Shicheng Xu, Liang Pang, Zhenhua Dong, and Jun Xu. 2024. Bias and unfairness in information retrieval systems: New challenges in the llm era. InProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 6437–6447

2024
[36]

Wenxi Dai, Wujiang Xu, Pinhuan Wang, and Dimitris N Metaxas. 2025. Token-Controlled Re-ranking for Sequential Recommendation via LLMs. arXiv preprint arXiv:2511.17913(2025)

2025
[37]

Anindya Bijoy Das and Shahnewaz Karim Sakib. 2024. Unveiling and Mitigating Bias in Large Language Model Recommendations: A Path to Fairness.arXiv preprint arXiv:2409.10825(2024)

2024
[38]

Badhan Chandra Das, M Hadi Amini, and Yanzhao Wu. 2025. Security and privacy challenges of large language models: A survey.Comput. Surveys 57, 6 (2025), 1–39

2025
[39]

Yashar Deldjoo. 2024. Understanding biases in ChatGPT-based recommender systems: Provider fairness, temporal stability, and recency.ACM Transactions on Recommender Systems(2024)

2024
[40]

Yashar Deldjoo and Tommaso Di Noia. 2025. Cfairllm: Consumer fairness evaluation in large-language model recommender system.ACM Transactions on Intelligent Systems and Technology(2025)

2025
[41]

Yashar Deldjoo, Nikhil Mehta, Maheswaran Sathiamoorthy, Shuai Zhang, Pablo Castells, and Julian McAuley. 2025. Toward Holistic Evaluation of Recommender Systems Powered by Generative Models. InProceedings of the 48th International ACM SIGIR Conference on Research and Developme...

2025
[42]

Yashar Deldjoo and Fatemeh Nazary. 2024. A Normative Framework for Benchmarking Consumer Fairness in Large Language Model Recommender System.arXiv preprint arXiv:2405.02219(2024)

2024
[43]

Jiaxin Deng, Shiyao Wang, Kuo Cai, Lejian Ren, Qigen Hu, Weifeng Ding, Qiang Luo, and Guorui Zhou. 2025. Onerec: Unifying retrieve and rank with generative recommender and iterative preference alignment.arXiv preprint arXiv:2502.18965(2025)

2025 arXiv
[44]

Dario Di Palma. 2023. Retrieval-augmented recommender system: Enhancing recommender systems with large language models. InProceedings of the 17th ACM Conference on Recommender Systems. 1369–1373

2023
[45]

David Dohan, Winnie Xu, Aitor Lewkowycz, Jacob Austin, David Bieber, Raphael Gontijo Lopes, Yuhuai Wu, Henryk Michalewski, Rif A Saurous, Jascha Sohl-Dickstein, et al. 2022. Language model cascades.arXiv preprint arXiv:2207.10342(2022)

2022
[46]

Shen Dong, Shaochen Xu, Pengfei He, Yige Li, Jiliang Tang, Tianming Liu, Hui Liu, and Zhen Xiang. 2025. Memory Injection Attacks on LLM Agents via Query-Only Interaction.arXiv preprint arXiv:2503.03704(2025)

2025
[47]

Yilun Du, Shuang Li, Antonio Torralba, Joshua B Tenenbaum, and Igor Mordatch. 2024. Improving factuality and reasoning in language models through multiagent debate. InForty-first international conference on machine learning

2024
[48]

Danial Ebrat, Eli Paradalis, and Luis Rueda. 2024. Lusifer: LLM-based user simulated feedback environment for online recommender systems. arXiv preprint arXiv:2405.13362(2024)

2024
[49]

Qamar EL Maazouzi, Asmaâ Retbi, and Samir Bennani. 2024. Optimizing Recommendation Systems in E-Learning: Synergistic Integration of Lang Chain, GPT Models, and Retrieval Augmented Generation (RAG). InInternational Conference on Smart Applications and Data Analysis. Springer, ...

2024
[50]

Chenxiao Fan, Chongming Gao, Yaxin Gong, Haoyan Liu, Fuli Feng, and Xiangnan He. 2026. Uncertainty-aware Generative Recommendation. arXiv preprint arXiv:2602.11719(2026)

2026 arXiv
[51]

Chenxiao Fan, Chongming Gao, Wentao Shi, Yaxin Gong, Zihao Zhao, and Fuli Feng. 2025. Fine-grained List-wise Alignment for Generative Medication Recommendation.arXiv preprint arXiv:2505.20218(2025)

2025 arXiv
[52]

Wenqi Fan, Xiangyu Zhao, Xiao Chen, Jingran Su, Jingtong Gao, Lin Wang, Qidong Liu, Yiqi Wang, Han Xu, Lei Chen, et al. 2022. A comprehensive survey on trustworthy recommender systems.arXiv preprint arXiv:2209.10117(2022)

2022
[53]

Shanshan Feng, Haoming Lyu, Fan Li, Zhu Sun, and Caishun Chen. 2024. Where to move next: Zero-shot generalization of llms for next poi recommendation. In2024 IEEE Conference on Artificial Intelligence (CAI). IEEE, 1530–1535

2024
[54]

Miriam Fernandez and Alejandro Bellogin. 2020. Recommender Systems and Misinformation: The Problem or the Solution?. InOHARS Workshop. 14th ACM Conference on Recommender Systems. OHARS Workshop. 14th ACM Conference on Recommender Systems. https://oro.open.ac.uk/72186/ Conferen...

2020
[55]

Giorgos Filandrianos, Angeliki Dimitriou, Maria Lymperaiou, Konstantinos Thomas, and Giorgos Stamou. 2025. Bias Beware: The Impact of Cognitive Biases on LLM-Driven Product Recommendations.arXiv preprint arXiv:2502.01349(2025)

2025
[56]

Luke Friedman, Sameer Ahuja, David Allen, Zhenning Tan, Hakim Sidahmed, Changbo Long, Jun Xie, Gabriel Schubiner, Ajay Patel, Harsh Lara, et al. 2023. Leveraging large language models in conversational recommender systems.arXiv preprint arXiv:2305.07961(2023)

2023
[57]

Isabel O Gallegos, Ryan A Rossi, Joe Barrow, Md Mehrab Tanjim, Sungchul Kim, Franck Dernoncourt, Tong Yu, Ruiyi Zhang, and Nesreen K Ahmed. 2024. Bias and fairness in large language models: A survey.Computational Linguistics50, 3 (2024), 1097–1179

2024
[58]

Chongming Gao, Ruijun Chen, Shuai Yuan, Kexin Huang, Yuanqing Yu, and Xiangnan He. 2025. Sprec: Self-play to debias llm-based recommendation. InProceedings of the ACM on Web Conference 2025. 5075–5084

2025
[59]

Chongming Gao, Mengyao Gao, Chenxiao Fan, Shuai Yuan, Wentao Shi, and Xiangnan He. 2025. Process-supervised llm recommenders via flow-guided tuning. InProceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1934–1943

2025
[60]

Chongming Gao, Wenqiang Lei, Xiangnan He, Maarten De Rijke, and Tat-Seng Chua. 2021. Advances and challenges in conversational recommender systems: A survey.AI open2 (2021), 100–126

2021
[61]

Jingtong Gao, Bo Chen, Xiangyu Zhao, Weiwen Liu, Xiangyang Li, Yichao Wang, Zijian Zhang, Wanyu Wang, Yuyang Ye, Shanru Lin, et al. 2024. Llm-enhanced reranking in recommender systems.arXiv preprint arXiv:2406.12433(2024)

2024
[62]

Shen Gao, Yifan Wang, Jiabao Fang, Lisi Chen, Peng Han, and Shuo Shang. 2024. DRE: Generating Recommendation Explanations by Aligning Large Language Models at Data-level.arXiv preprint arXiv:2404.06311(2024)

2024
[63]

Yunfan Gao, Tao Sheng, Youlin Xiang, Yun Xiong, Haofen Wang, and Jiawei Zhang. 2023. Chat-rec: Towards interactive and explainable llms-augmented recommender system.arXiv preprint arXiv:2303.14524(2023)

2023
[64]

Yingqiang Ge, Shuchang Liu, Zuohui Fu, Juntao Tan, Zelong Li, Shuyuan Xu, Yunqi Li, Yikun Xian, and Yongfeng Zhang. 2024. A survey on trustworthy recommender systems.ACM Transactions on Recommender Systems3, 2 (2024), 1–68

2024
[65]

Yingqiang Ge, Shuya Zhao, Honglu Zhou, Changhua Pei, Fei Sun, Wenwu Ou, and Yongfeng Zhang. 2020. Understanding echo chambers in e-commerce recommender systems. InProceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval. 2261–2270

2020
[66]

Shijie Geng, Zuohui Fu, Juntao Tan, Yingqiang Ge, Gerard De Melo, and Yongfeng Zhang. 2022. Path language modeling over knowledge graphsfor explainable recommendation. InProceedings of the ACM web conference 2022. 946–955

2022
[67]

Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2022. Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5). InProceedings of the 16th ACM conference on recommender systems. 299–315

2022
[68]

Preetam Ghosh and Vaishali Sadaphal. 2023. JobRecoGPT–Explainable job recommendations using LLMs.arXiv preprint arXiv:2309.11805(2023)

2023
[69]

Yuqi Gong, Xichen Ding, Yehui Su, Kaiming Shen, Zhongyi Liu, and Guannan Zhang. 2023. An unified search and recommendation foundation model for cold-start scenario. InProceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4595–4601

2023
[70]

Yaxin Gong, Chongming Gao, Chenxiao Fan, Wenjie Wang, Fuli Feng, and Xiangnan He. 2026. Breaking User-Centric Agency: A Tri-Party Framework for Agent-Based Recommendation.arXiv preprint arXiv:2603.10673(2026)

2026 arXiv
[71]

Shengkang Gu, Jiahao Liu, Dongsheng Li, Guangping Zhang, Mingzhe Han, Hansu Gu, Peng Zhang, Ning Gu, Li Shang, and Tun Lu. 2025. LLM-Based User Simulation for Low-Knowledge Shilling Attacks on Recommender Systems.arXiv preprint arXiv:2505.13528(2025)

2025
[72]

Ihsan Gunes, Cihan Kaleli, Alper Bilge, and Huseyin Polat. 2014. Shilling attacks against recommender systems: a comprehensive survey.Artificial Intelligence Review42, 4 (2014), 767–799

2014
[73]

Deepesh V Hada and Shirish K Shevade. 2021. Rexplug: Explainable recommendation using plug-and-play language model. InProceedings of the 44th international ACM SIGIR conference on research and development in information retrieval. 81–91

2021
[74]

F Maxwell Harper and Joseph A Konstan. 2015. The movielens datasets: History and context.Acm transactions on interactive intelligent systems (tiis)5, 4 (2015), 1–19

2015
[75]

Matthew J Hausknecht and Peter Stone. 2015. Deep Recurrent Q-Learning for Partially Observable MDPs.. InAAAI fall symposia, Vol. 45. 141

2015
[76]

Shirley Anugrah Hayati, Dongyeop Kang, Qingxiaoyang Zhu, Weiyan Shi, and Zhou Yu. 2020. Inspired: Toward sociable recommendation dialog systems. InProceedings of the 2020 conference on empirical methods in natural language processing (EMNLP). 8142–8152

2020
[77]

Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. Lightgcn: Simplifying and powering graph convolution network for recommendation. InProceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval. ...

2020
[78]

Diana C Hernandez-Bocanegra, Tim Donkers, and Jürgen Ziegler. 2020. Effects of argumentative explanation types on the perception of review-based recommendations. InAdjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization. 219–225

2020
[79]

Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks.arXiv preprint arXiv:1511.06939(2015)

2015 arXiv
[80]

Minjie Hong, Yan Xia, Zehan Wang, Jieming Zhu, Ye Wang, Sihang Cai, Xiaoda Yang, Quanyu Dai, Zhenhua Dong, Zhimeng Zhang, et al. 2025. EAGER-LLM: Enhancing Large Language Models as Recommenders through Exogenous Behavior-Semantic Integration. InProceedings of the ACM on Web Co...

2025
[81]

Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, and Wayne Xin Zhao. 2024. Large language models are zero-shot rankers for recommender systems. InEuropean Conference on Information Retrieval. Springer, 364–381

2024
[82]

Yu Hou and Rui Zhang. 2024. Enhancing Dietary Supplement Question Answer via Retrieval-Augmented Generation (RAG) with LLM.medRxiv (2024), 2024–09

2024
[83]

Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Liang Wang, Weizhu Chen, et al. 2022. Lora: Low-rank adaptation of large language models.Iclr1, 2 (2022), 3

2022
[84]

Yuhan Hu, Ziyu Lyu, Lu Bai, and Lixin Cui. 2025. FairWork: A Generic Framework For Evaluating Fairness In LLM-Based Job Recommender System. InProceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval. 3964–3968

2025
[85]

Zhiyu Hu, Yang Zhang, Minghao Xiao, Wenjie Wang, Fuli Feng, and Xiangnan He. 2025. Exact and efficient unlearning for large language model-based recommendation.IEEE Transactions on Knowledge and Data Engineering(2025)

2025
[86]

Wenyue Hua, Yingqiang Ge, Shuyuan Xu, Jianchao Ji, and Yongfeng Zhang. 2023. Up5: Unbiased foundation model for fairness-aware recommen- dation.arXiv preprint arXiv:2305.12090(2023)

2023
[87]

Wenyue Hua, Shuyuan Xu, Yingqiang Ge, and Yongfeng Zhang. 2023. How to index item ids for recommendation foundation models. InProceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region. 195–204

2023
[88]

Feiran Huang, Yuanchen Bei, Zhenghang Yang, Junyi Jiang, Hao Chen, Qijie Shen, Senzhang Wang, Fakhri Karray, and Philip S Yu. 2025. Large Language Model Simulator for Cold-Start Recommendation. InProceedings of the Eighteenth ACM International Conference on Web Search and Data...

2025
[89]

Hai Huang, Jiaming Mu, Neil Zhenqiang Gong, Qi Li, Bin Liu, and Mingwei Xu. 2021. Data poisoning attacks to deep learning based recommender systems.arXiv preprint arXiv:2101.02644(2021)

2021
[90]

Jie Huang, Xinyun Chen, Swaroop Mishra, Huaixiu Steven Zheng, Adams Wei Yu, Xinying Song, and Denny Zhou. 2023. Large language models cannot self-correct reasoning yet.arXiv preprint arXiv:2310.01798(2023)

2023 arXiv
[91]

Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, et al. 2025. A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions.ACM Transactions on Infor...

2025
[92]

Dietmar Jannach and Himan Abdollahpouri. 2023. A survey on multi-objective recommender systems.Frontiers in big Data6 (2023), 1157899

2023
[93]

Arjan JP Jeckmans, Michael Beye, Zekeriya Erkin, Pieter Hartel, Reginald L Lagendijk, and Qiang Tang. 2012. Privacy in recommender systems. In Social media retrieval. Springer, 263–281

2012
[94]

Nanshan Jia, Chenfei Yuan, Yuhang Wu, and Zeyu Zheng. 2025. Improving LLM Interpretability and Performance via Guided Embedding Refinement for Sequential Recommendation.arXiv preprint arXiv:2504.11658(2025)

2025
[95]

Chumeng Jiang, Jiayin Wang, Weizhi Ma, Charles LA Clarke, Shuai Wang, Chuhan Wu, and Min Zhang. 2025. Beyond Utility: Evaluating LLM as Recommender. InProceedings of the ACM on Web Conference 2025. 3850–3862

2025
[96]

Meng Jiang, Keqin Bao, Jizhi Zhang, Wenjie Wang, Zhengyi Yang, Fuli Feng, and Xiangnan He. 2024. Item-side fairness of large language model-based recommendation system. InProceedings of the ACM Web Conference 2024. 4717–4726

2024
[97]

Yizhu Jiao, Siru Ouyang, Ming Zhong, Yunyi Zhang, Linyi Ding, Sizhe Zhou, and Jiawei Han. 2025. Retrieval and Structuring Augmented Generation with LLMs for Web Applications. InCompanion Proceedings of the ACM on Web Conference 2025. 25–28

2025
[98]

Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In2018 IEEE international conference on data mining (ICDM). IEEE, 197–206

2018
[99]

Kirandeep Kaur, Vinayak Gupta, Manya Chadha, and Chirag Shah. 2026. Efficient and responsible adaptation of large language models for robust top-k recommendations.ACM Transactions on Recommender Systems4, 3 (2026), 1–31

2026
[100]

Zixuan Ke, Fangkai Jiao, Yifei Ming, Xuan-Phi Nguyen, Austin Xu, Do Xuan Long, Minzhi Li, Chengwei Qin, Peifeng Wang, Silvio Savarese, et al
[101]

A survey of frontiers in llm reasoning: Inference scaling, learning to reason, and agentic systems.arXiv preprint arXiv:2504.09037(2025)

2025
[102]

Tina Khezresmaeilzadeh, Jiang Zhang, Dimitrios Andreadis, and Konstantinos Psounis. 2025. Preserving privacy and utility in llm-based product recommendations.arXiv preprint arXiv:2505.00951(2025)

2025
[103]

Jangho Kim, Simyung Chang, and Nojun Kwak. 2021. PQK: model compression via pruning, quantization, and knowledge distillation.arXiv preprint arXiv:2106.14681(2021)

2021
[104]

Sein Kim, Hongseok Kang, Seungyoon Choi, Donghyun Kim, Minchul Yang, and Chanyoung Park. 2024. Large language models meet collaborative filtering: An efficient all-round llm-based recommender system. InProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Da...

2024
[105]

Bart P Knijnenburg, Martijn C Willemsen, Zeno Gantner, Hakan Soncu, and Chris Newell. 2012. Explaining the user experience of recommender systems.User modeling and user-adapted interaction22, 4 (2012), 441–504

2012
[106]

Yehuda Koren. 2009. Collaborative filtering with temporal dynamics. InProceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. 447–456

2009
[107]

Pranath Reddy Kumbam, Sohaib Uddin Syed, Prashanth Thamminedi, Suhas Harish, Ian Perera, and Bonnie J Dorr. 2025. Exploiting explainability to design adversarial attacks and evaluate attack resilience in hate-speech detection models. InProceedings of the International AAAI Con...

2025
[108]

Genki Kusano. 2024. Data Augmentation using Reverse Prompt for Cost-Efficient Cold-Start Recommendation. InProceedings of the 18th ACM Conference on Recommender Systems. 861–865

2024
[109]

Genki Kusano, Kosuke Akimoto, and Kunihiro Takeoka. 2024. Are Longer Prompts Always Better? Prompt Selection in Large Language Models for Recommendation Systems.arXiv preprint arXiv:2412.14454(2024)

2024
[110]

Shyong K Lam and John Riedl. 2004. Shilling recommender systems for fun and profit. InProceedings of the 13th international conference on World Wide Web. 393–402

2004
[111]

Jouni Lampinen. 2000. Multiobjective nonlinear pareto-optimization.Pre-investigation Report, Lappeenranta University of Technology114 (2000), 125

2000
[112]

Tomo Lazovich. 2023. Filter bubbles and affective polarization in user-personalized large language model outputs. InProceedings on. PMLR, 29–37

2023
[113]

Wenqiang Lei, Xiangnan He, Yisong Miao, Qingyun Wu, Richang Hong, Min-Yen Kan, and Tat-Seng Chua. 2020. Estimation-action-reflection: Towards deep interaction between conversational and recommender systems. InProceedings of the 13th international conference on web search and d...

2020
[114]

Yuxuan Lei, Jianxun Lian, Jing Yao, Xu Huang, Defu Lian, and Xing Xie. 2024. Recexplainer: Aligning large language models for explaining recommendation models. InProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1530–1541

2024
[115]

Bo Li, Peng Qi, Bo Liu, Shuai Di, Jingen Liu, Jiquan Pei, Jinfeng Yi, and Bowen Zhou. 2023. Trustworthy AI: From principles to practices.Comput. Surveys55, 9 (2023), 1–46

2023
[116]

Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017. Neural attentive session-based recommendation. InProceedings of the 2017 ACM on Conference on Information and Knowledge Management. 1419–1428

2017
[117]

Kaihong Li, Huichi Zhou, Bin Ma, and Fangjun Huang. 2026. SemanticShield: LLM-powered audits expose shilling attacks in recommender systems. InICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 5381–5385

2026
[118]

Lei Li, Li Chen, and Ruihai Dong. 2021. Caesar: context-aware explanation based on supervised attention for service recommendations.Journal of Intelligent Information Systems57, 1 (2021), 147–170

2021
[119]

Lei Li, Yongfeng Zhang, and Li Chen. 2023. Prompt distillation for efficient llm-based recommendation. InProceedings of the 32nd ACM International Conference on Information and Knowledge Management. 1348–1357

2023
[120]

Qingyao Li, Wei Xia, Kounianhua Du, Qiji Zhang, Weinan Zhang, Ruiming Tang, and Yong Yu. 2024. Learning Structure and Knowledge Aware Representation with Large Language Models for Concept Recommendation.arXiv preprint arXiv:2405.12442(2024)

2024
[121]

Raymond Li, Samira Ebrahimi Kahou, Hannes Schulz, Vincent Michalski, Laurent Charlin, and Chris Pal. 2018. Towards deep conversational recommendations.Advances in neural information processing systems31 (2018)

2018
[122]

Tianlin Li, Xiaoyu Zhang, Chao Du, Tianyu Pang, Qian Liu, Qing Guo, Chao Shen, and Yang Liu. 2024. Your large language model is secretly a fairness proponent and you should prompt it like one.arXiv preprint arXiv:2402.12150(2024)

2024
[123]

Xinhang Li, Chong Chen, Xiangyu Zhao, Yong Zhang, and Chunxiao Xing. 2023. E4srec: An elegant effective efficient extensible solution of large language models for sequential recommendation.arXiv preprint arXiv:2312.02443(2023)

2023
[124]

Xinyi Li, Yongfeng Zhang, and Edward C Malthouse. 2023. A preliminary study of chatgpt on news recommendation: Personalization, provider fairness, fake news.arXiv preprint arXiv:2306.10702(2023)

2023
[125]

Yuxi Li. 2017. Deep reinforcement learning: An overview.arXiv preprint arXiv:1701.07274(2017)

2017 arXiv
[126]

Yuyuan Li, Xiaohua Feng, Chaochao Chen, and Qiang Yang. 2024. A survey on recommendation unlearning: Fundamentals, taxonomy, evaluation, and open questions.arXiv preprint arXiv:2412.12836(2024)

2024
[127]

Yu Li, Yixiao Liu, and Tetsuya Furukawa. 2023. Integrating Prior Knowledge from Meta-Learning and Large Language Models for Cold-Start Recommendation. (2023)

2023
[128]

Yuhan Li, Xinni Zhang, Linhao Luo, Heng Chang, Yuxiang Ren, Irwin King, and Jia Li. 2025. G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation. InProceedings of the ACM on Web Conference 2025. 240–251

2025
[129]

Yichuan Li, Xinyang Zhang, Chenwei Zhang, Mao Li, Tianyi Liu, Pei Chen, Yifan Gao, Kyumin Lee, Kaize Ding, Zhengyang Wang, et al. 2025. ALERT: An LLM-powered Benchmark for Automatic Evaluation of Recommendation Explanations. InProceedings of the 2025 Conference of the Nations ...

2025
[130]

Zhuoyang Li, Yanlai Wu, Yao Li, Xinning Gui, and Yuhan Luo. 2026. Privacy Control in Conversational LLM Platforms: A Walkthrough Study. arXiv preprint arXiv:2602.10684(2026)

2026
[131]

Jianxun Lian, Yuxuan Lei, Xu Huang, Jing Yao, Wei Xu, and Xing Xie. 2024. Recai: Leveraging large language models for next-generation recommender systems. InCompanion Proceedings of the ACM Web Conference 2024. 1031–1034. Manuscript submitted to ACM 40 Bohao Wang et al

2024
[132]

Jiayi Liao, Xiangnan He, Ruobing Xie, Jiancan Wu, Yancheng Yuan, Xingwu Sun, Zhanhui Kang, and Xiang Wang. 2024. RosePO: Aligning LLM-based Recommenders with Human Values.arXiv preprint arXiv:2410.12519(2024)

2024
[133]

Jiayi Liao, Sihang Li, Zhengyi Yang, Jiancan Wu, Yancheng Yuan, Xiang Wang, and Xiangnan He. 2024. Llara: Large language-recommendation assistant. InProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1785–1795

2024
[134]

Jan Malte Lichtenberg, Alexander Buchholz, and Pola Schwöbel. 2024. Large language models as recommender systems: A study of popularity bias. arXiv preprint arXiv:2406.01285(2024)

2024
[135]

Guanyu Lin, Feng Liang, Weike Pan, and Zhong Ming. 2020. Fedrec: Federated recommendation with explicit feedback.IEEE Intelligent Systems36, 5 (2020), 21–30

2020
[136]

Jianghao Lin, Xinyi Dai, Yunjia Xi, Weiwen Liu, Bo Chen, Hao Zhang, Yong Liu, Chuhan Wu, Xiangyang Li, Chenxu Zhu, et al. 2025. How can recommender systems benefit from large language models: A survey.ACM Transactions on Information Systems43, 2 (2025), 1–47

2025
[137]

Xinyu Lin, Wenjie Wang, Yongqi Li, Fuli Feng, See-Kiong Ng, and Tat-Seng Chua. 2024. Bridging items and language: A transition paradigm for large language model-based recommendation. InProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1816–1826

2024
[138]

Xinyu Lin, Hanqing Zeng, Hanchao Yu, Yinglong Xia, Jiang Zhang, Aashu Singh, Fei Liu, Wenjie Wang, Fuli Feng, Tat-Seng Chua, et al. 2026. Verifiable Reasoning for LLM-based Generative Recommendation.arXiv preprint arXiv:2603.07725(2026)

2026
[139]

Yujie Lin, Chenyang Wang, Zhumin Chen, Zhaochun Ren, Xin Xin, Qiang Yan, Maarten de Rijke, Xiuzhen Cheng, and Pengjie Ren. 2023. A self-correcting sequential recommender. InProceedings of the ACM Web Conference 2023. 1283–1293

2023
[140]

Zachary C Lipton. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue16, 3 (2018), 31–57

2018
[141]

Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, et al
[142]

Deepseek-v3 technical report.arXiv preprint arXiv:2412.19437(2024)

2024 arXiv
[143]

Junling Liu, Chao Liu, Peilin Zhou, Qichen Ye, Dading Chong, Kang Zhou, Yueqi Xie, Yuwei Cao, Shoujin Wang, Chenyu You, et al. 2023. Llmrec: Benchmarking large language models on recommendation task.arXiv preprint arXiv:2308.12241(2023)

2023
[144]

Jiahao Liu, Yiyang Shao, Peng Zhang, Dongsheng Li, Hansu Gu, Chao Chen, Longzhi Du, Tun Lu, and Ning Gu. 2025. Filtering Discomforting Recommendations with Large Language Models. InProceedings of the ACM on Web Conference 2025. 3639–3650

2025
[145]

Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.ACM computing surveys55, 9 (2023), 1–35

2023
[146]

Qidong Liu, Xian Wu, Wanyu Wang, Yejing Wang, Yuanshao Zhu, Xiangyu Zhao, Feng Tian, and Yefeng Zheng. 2025. Llmemb: Large language model can be a good embedding generator for sequential recommendation. InProceedings of the AAAI Conference on Artificial Intelligence, Vol. 39. ...

2025
[147]

Qidong Liu, Xian Wu, Yejing Wang, Zijian Zhang, Feng Tian, Yefeng Zheng, and Xiangyu Zhao. 2024. Llm-esr: Large language models enhancement for long-tailed sequential recommendation.Advances in Neural Information Processing Systems37 (2024), 26701–26727

2024
[148]

Ruochen Liu, Hao Chen, Yuanchen Bei, Zheyu Zhou, Lijia Chen, Qijie Shen, Feiran Huang, Fakhri Karray, and Senzhang Wang. 2025. FilterLLM: Text-To-Distribution LLM for Billion-Scale Cold-Start Recommendation.arXiv preprint arXiv:2502.16924(2025)

2025
[149]

Wei Liu, Baisong Liu, Jiangcheng Qin, Xueyuan Zhang, Weiming Huang, and Yangyang Wang. 2025. Fairness identification of large language models in recommendation.Scientific Reports15, 1 (2025), 5516

2025
[150]

Hongyu Lu, Weizhi Ma, Yifan Wang, Min Zhang, Xiang Wang, Yiqun Liu, Tat-Seng Chua, and Shaoping Ma. 2023. User perception of recommendation explanation: Are your explanations what users need?ACM Transactions on Information Systems41, 2 (2023), 1–31

2023
[151]

Sijin Lu, Zhibo Man, Fangyuan Luo, and Jun Wu. 2025. Dual Debiasing in LLM-based Recommendation. InProceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2685–2689

2025
[152]

Wensheng Lu, Jianxun Lian, Wei Zhang, Guanghua Li, Mingyang Zhou, Hao Liao, and Xing Xie. 2024. Aligning large language models for controllable recommendations. InProceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 8159–8172

2024
[153]

Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 2022. Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity. InProceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Vol...

2022
[154]

Sichun Luo, Bowei He, Haohan Zhao, Wei Shao, Yanlin Qi, Yinya Huang, Aojun Zhou, Yuxuan Yao, Zongpeng Li, Yuanzhang Xiao, et al. 2025. Recranker: Instruction tuning large language model as ranker for top-k recommendation.ACM Transactions on Information Systems43, 5 (2025), 1–31

2025
[155]

Yucong Luo, Mingyue Cheng, Hao Zhang, Junyu Lu, and Enhong Chen. 2024. Unlocking the potential of large language models for explainable recommendations. InInternational Conference on Database Systems for Advanced Applications. Springer, 286–303

2024
[156]

Hanjia Lyu, Song Jiang, Hanqing Zeng, Yinglong Xia, Qifan Wang, Si Zhang, Ren Chen, Christopher Leung, Jiajie Tang, and Jiebo Luo. 2023. Llm-rec: Personalized recommendation via prompting large language models.arXiv preprint arXiv:2307.15780(2023)

2023
[157]

Hao Ma, Haixuan Yang, Michael R Lyu, and Irwin King. 2008. Sorec: social recommendation using probabilistic matrix factorization. InProceedings of the 17th ACM conference on Information and knowledge management. 931–940. Manuscript submitted to ACM Trustworthy Recommendation i...

2008
[158]

Qiyao Ma, Xubin Ren, and Chao Huang. 2024. Xrec: Large language models for explainable recommendation.arXiv preprint arXiv:2406.02377 (2024)

2024
[159]

Tianhui Ma, Yuan Cheng, Hengshu Zhu, and Hui Xiong. 2023. Large language models are not stable recommender systems.arXiv preprint arXiv:2312.15746(2023)

2023
[160]

Ulysse Maes, Lien Michiels, and Annelien Smets. 2025. Mitigating Misleadingness in LLM-Generated Natural Language Explanations for Recommender Systems: Ensuring Broad Truthfulness Through Factuality and Faithfulness. InProceedings of the 29th Annual ACM Conference on Intellige...

2025
[161]

Anna-Katharina Meßmer and Martin Degeling. 2023. Auditing Recommender Systems–Putting the DSA into practice with a risk-scenario-based approach.arXiv preprint arXiv:2302.04556(2023)

2023
[162]

2019.An introduction to game-theoretic modelling

Mike Mesterton-Gibbons. 2019.An introduction to game-theoretic modelling. Vol. 37. American Mathematical Soc

2019
[163]

Shuyi Miao, Wangjie Qiu, Shengda Zhuo, Fei Shen, Dan Lin, Xingtong Yu, Chua Tat-Seng, and Zhiming Zheng. 2026. UniDetect: LLM-Driven Universal Fraud Detection across Heterogeneous Blockchains.arXiv preprint arXiv:2604.12329(2026)

2026 arXiv
[164]

Silvia Milano, Mariarosaria Taddeo, and Luciano Floridi. 2020. Recommender systems and their ethical challenges.Ai & Society35, 4 (2020), 957–967

2020
[165]

John Morris, Volodymyr Kuleshov, Vitaly Shmatikov, and Alexander M Rush. 2023. Text embeddings reveal (almost) as much as text. InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 12448–12460

2023
[166]

John X Morris, Eli Lifland, Jin Yong Yoo, Jake Grigsby, Di Jin, and Yanjun Qi. 2020. Textattack: A framework for adversarial attacks, data augmentation, and adversarial training in nlp.arXiv preprint arXiv:2005.05909(2020)

2020
[167]

Cataldo Musto, Pasquale Lops, Marco de Gemmis, and Giovanni Semeraro. 2019. Justifying recommendations through aspect-based sentiment analysis of users reviews. InProceedings of the 27th ACM conference on user modeling, adaptation and personalization. 4–12

2019
[168]

Dina Nawara, Ahmed Aly, and Rasha Kashef. 2024. Shilling attacks and fake reviews injection: Principles, models, and datasets.IEEE Transactions on Computational Social Systems(2024)

2024
[169]

Fatemeh Nazary, Yashar Deldjoo, Tommaso Di Noia, and Eugenio Di Sciascio. 2025. Stealthy LLM-Driven Data Poisoning Attacks Against Embedding-Based Retrieval-Augmented Recommender Systems. InAdjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Persona...

2025
[170]

Fatemeh Nazary, Yashar Deldjoo, and Tommaso di Noia. 2025. Poison-rag: Adversarial data poisoning attacks on retrieval-augmented generation in recommender systems. InEuropean Conference on Information Retrieval. Springer, 239–251

2025
[171]

Minh-Anh Nguyen, Bao Nguyen, Tuan Anh Hoang, Duc-Trong Le, Dung D Le, et al . 2025. Enhancing Graph-based Recommendations with Majority-Voting LLM-Rerank Augmentation.arXiv preprint arXiv:2507.21563(2025)

2025 arXiv
[172]

Thanh Toan Nguyen, Nguyen Quoc Viet Hung, Thanh Tam Nguyen, Thanh Trung Huynh, Thanh Thi Nguyen, Matthias Weidlich, and Hongzhi Yin. 2024. Manipulating recommender systems: A survey of poisoning attacks and countermeasures.Comput. Surveys57, 1 (2024), 1–39

2024
[173]

Liangbo Ning, Wenqi Fan, and Qing Li. 2025. Exploring Backdoor Attack and Defense for LLM-empowered Recommendations.arXiv preprint arXiv:2504.11182(2025)

2025
[174]

Liangbo Ning, Wenqi Fan, and Qing Li. 2025. Retrieval-Augmented Purifier for Robust LLM-Empowered Recommendation.arXiv preprint arXiv:2504.02458(2025)

2025
[175]

Liang-bo Ning, Shijie Wang, Wenqi Fan, Qing Li, Xin Xu, Hao Chen, and Feiran Huang. 2024. Cheatagent: Attacking llm-empowered recommender systems via llm agent. InProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2284–2295

2024
[176]

Jieun Park, Sangyeon Kim, and Sangwon Lee. 2023. A user preference and intent extraction framework for explainable conversational recommender systems. InCompanion Proceedings of the 2023 ACM SIGCHI Symposium on Engineering Interactive Computing Systems. 16–23

2023
[177]

Denis Parra and Peter Brusilovsky. 2015. User-controllable personalization: A case study with SetFusion.International Journal of Human-Computer Studies78 (2015), 43–67

2015
[178]

Qiyao Peng, Hongtao Liu, Hua Huang, Qing Yang, and Minglai Shao. 2025. A survey on llm-powered agents for recommender systems.arXiv preprint arXiv:2502.10050(2025)

2025
[179]

Yicui Peng, Hao Chen, Chingsheng Lin, Guo Huang, Jinrong Hu, Hui Guo, Bin Kong, Shu Hu, Xi Wu, and Xin Wang. 2024. Uncertainty-Aware Explainable Recommendation with Large Language Models.arXiv preprint arXiv:2402.03366(2024)

2024
[180]

Yingtao Peng, Chen Gao, Yu Zhang, Tangpeng Dan, Xiaoyi Du, Hengliang Luo, Yong Li, and Xiaofeng Meng. 2025. Denoising alignment with large language model for recommendation.ACM Transactions on Information Systems43, 2 (2025), 1–35

2025
[181]

Alessandro Petruzzelli, Cataldo Musto, Lucrezia Laraspata, Ivan Rinaldi, Marco de Gemmis, Pasquale Lops, and Giovanni Semeraro. 2024. Instructing and prompting large language models for explainable cross-domain recommendations. InProceedings of the 18th ACM Conference on Recom...

2024
[182]

Felipe M Polo, Ronald Xu, Lucas Weber, Mírian Silva, Onkar Bhardwaj, Leshem Choshen, Allysson F de Oliveira, Yuekai Sun, and Mikhail Yurochkin
[183]

Efficient multi-prompt evaluation of llms.Advances in Neural Information Processing Systems37 (2024), 22483–22512

2024
[184]

Martin L Puterman. 1990. Markov decision processes.Handbooks in operations research and management science2 (1990), 331–434

1990
[185]

Zhangchi Qiu, Linhao Luo, Shirui Pan, and Alan Wee-Chung Liew. 2024. Unveiling User Preferences: A Knowledge Graph and LLM-Driven Approach for Conversational Recommendation.arXiv preprint arXiv:2411.14459(2024). Manuscript submitted to ACM 42 Bohao Wang et al

2024
[186]

Haohao Qu, Wenqi Fan, Zihuai Zhao, and Qing Li. 2024. Tokenrec: learning to tokenize id for llm-based generative recommendation.arXiv preprint arXiv:2406.10450(2024)

2024
[187]

Behnam Rahdari, Hao Ding, Ziwei Fan, Yifei Ma, Zhuotong Chen, Anoop Deoras, and Branislav Kveton. 2024. Logic-scaffolding: Personalized aspect-instructed recommendation explanation generation using llms. InProceedings of the 17th ACM International Conference on Web Search and ...

2024
[188]

Hamed Rahimian and Sanjay Mehrotra. 2019. Distributionally robust optimization: A review.arXiv preprint arXiv:1908.05659(2019)

2019
[189]

Shashank Rajput, Nikhil Mehta, Anima Singh, Raghunandan Hulikal Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Tran, Jonah Samost, et al. 2023. Recommender systems with generative retrieval.Advances in Neural Information Processing Systems36 (2023), 10299–10315

2023
[190]

Shaina Raza and Chen Ding. 2022. News recommender system: a review of recent progress, challenges, and opportunities.Artificial Intelligence Review55, 1 (2022), 749–800

2022
[191]

Amirhossein Razavi, Mina Soltangheis, Negar Arabzadeh, Sara Salamat, Morteza Zihayat, and Ebrahim Bagheri. 2025. Benchmarking prompt sensitivity in large language models. InEuropean Conference on Information Retrieval. Springer, 303–313

2025
[192]

Xubin Ren, Wei Wei, Lianghao Xia, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, and Chao Huang. 2024. Representation learning with large language models for recommendation. InProceedings of the ACM web conference 2024. 3464–3475

2024
[193]

Yankun Ren, Zhongde Chen, Xinxing Yang, Longfei Li, Cong Jiang, Lei Cheng, Bo Zhang, Linjian Mo, and Jun Zhou. 2024. Enhancing sequential recommenders with augmented knowledge from aligned large language models. InProceedings of the 47th International ACM SIGIR Conference on R...

2024
[194]

Paul Resnick and Hal R Varian. 1997. Recommender systems.Commun. ACM40, 3 (1997), 56–58

1997
[195]

Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh. 2020. Beyond accuracy: Behavioral testing of NLP models with CheckList. arXiv preprint arXiv:2005.04118(2020)

2020
[196]

Mihaela Rotar, Theresia Veronika Rampisela, and Maria Maistro. 2026. Can Fairness Be Prompted? Prompt-Based Debiasing Strategies in High-Stakes Recommendations.arXiv preprint arXiv:2603.12935(2026)

2026
[197]

Panjawat Rungtranont and Janjao Mongkolnavin. 2024. Using Large Language Models as user Interests Interpretation for Solving Cold-Start Item Recommendation. In2024 23rd International Symposium on Communications and Information Technologies (ISCIT). IEEE, 36–40

2024
[198]

Bhavika Sachdeva, Harshita Rathee, Sristi, Arun Sharma, and Witold Wydmański. 2024. Machine unlearning for recommendation systems: An insight. InInternational Conference On Innovative Computing And Communication. Springer, 415–430

2024
[199]

Chandan Kumar Sah, Xiaoli Lian, Tony Xu, and Li Zhang. 2025. FairEval: Evaluating Fairness in LLM-Based Recommendations with Personality Awareness.arXiv preprint arXiv:2504.07801(2025)

2025
[200]

Shahnewaz Karim Sakib and Anindya Bijoy Das. 2024. Challenging fairness: A comprehensive exploration of bias in llm-based recommendations. In2024 IEEE International Conference on Big Data (BigData). IEEE, 1585–1592

2024
[201]

Keigo Sakurai, Ren Togo, Takahiro Ogawa, and Miki Haseyama. 2025. Llm is knowledge graph reasoner: Llm’s intuition-aware knowledge graph reasoning for cold-start sequential recommendation. InEuropean Conference on Information Retrieval. Springer, 263–278

2025
[202]

Scott Sanner, Krisztian Balog, Filip Radlinski, Ben Wedin, and Lucas Dixon. 2023. Large language models are competitive near cold-start recommenders for language-and item-based preferences. InProceedings of the 17th ACM conference on recommender systems. 890–896

2023
[203]

Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims. 2016. Recommendations as treatments: Debiasing learning and evaluation. Ininternational conference on machine learning. PMLR, 1670–1679

2016
[204]

Amit Sharma, Hua Li, Xue Li, and Jian Jiao. 2024. Optimizing Novelty of Top-k Recommendations using Large Language Models and Reinforcement Learning. InProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 5669–5679

2024
[205]

Guangsi Shi, Xiaofeng Deng, Linhao Luo, Lijuan Xia, Lei Bao, Bei Ye, Fei Du, Shirui Pan, and Yuxiao Li. 2024. Llm-powered explanations: Unraveling recommendations through subgraph reasoning.arXiv preprint arXiv:2406.15859(2024)

2024
[206]

Shaoyun Shi, Hanxiong Chen, Weizhi Ma, Jiaxin Mao, Min Zhang, and Yongfeng Zhang. 2020. Neural logic reasoning. InProceedings of the 29th ACM International Conference on Information & Knowledge Management. 1365–1374

2020
[207]

Ryotaro Shimizu, Takashi Wada, Yu Wang, Johannes Kruse, Sean O’Brien, Sai HtaungKham, Linxin Song, Yuya Yoshikawa, Yuki Saito, Fugee Tsung, et al. 2025. Disentangling likes and dislikes in personalized generative explainable recommendation. InProceedings of the ACM on Web Conference

2025
[208]

Reza Shokri, Martin Strobel, and Yair Zick. 2021. On the privacy risks of model explanations. InProceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. 231–241

2021
[209]

Yubo Shu, Haonan Zhang, Hansu Gu, Peng Zhang, Tun Lu, Dongsheng Li, and Ning Gu. 2024. RAH! RecSys–Assistant–Human: A Human-Centered Recommendation Framework With LLM Agents.IEEE Transactions on Computational Social Systems(2024)

2024
[210]

Ítallo Silva, Leandro Marinho, Alan Said, and Martijn C Willemsen. 2024. Leveraging chatgpt for automated human-centered explanations in recommender systems. InProceedings of the 29th International Conference on Intelligent User Interfaces. 597–608

2024
[211]

Tianrui Song, Wenshuo Chao, and Hao Liu. 2024. Large language model enhanced hard sample identification for denoising recommendation.arXiv preprint arXiv:2409.10343(2024)

2024
[212]

Kyle Dylan Spurlock, Cagla Acun, Esin Saka, and Olfa Nasraoui. 2024. Chatgpt for conversational recommendation: Refining recommendations by reprompting with feedback.arXiv preprint arXiv:2401.03605(2024). Manuscript submitted to ACM Trustworthy Recommendation in the Era of Lar...

2024
[213]

Robin Staab, Mark Vero, Mislav Balunović, and Martin Vechev. 2023. Beyond memorization: Violating privacy via inference with large language models.arXiv preprint arXiv:2310.07298(2023)

2023
[214]

Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. InProceedings of the 28th ACM international conference on information and knowledge management. 1441–1450

2019
[215]

Youbang Sun, Zitao Li, Yaliang Li, and Bolin Ding. 2024. Improving lora in privacy-preserving federated learning.arXiv preprint arXiv:2403.12313 (2024)

2024
[216]

Yuqi Sun, Qidong Liu, Haiping Zhu, and Feng Tian. 2025. LLMSeR: Enhancing Sequential Recommendation via LLM-based Data Augmentation. arXiv preprint arXiv:2503.12547(2025)

2025
[217]

Yatong Sun, Bin Wang, Zhu Sun, and Xiaochun Yang. 2021. Does Every Data Instance Matter? Enhancing Sequential Recommendation by Eliminating Unreliable Data.. InIJCAI. 1579–1585

2021
[218]

Yatong Sun, Xiaochun Yang, Zhu Sun, Yan Wang, Bin Wang, and Xinghua Qu. 2025. LLM4RSR: Large Language Models as Data Correctors for Robust Sequential Recommendation. InProceedings of the AAAI Conference on Artificial Intelligence, Vol. 39. 12604–12612

2025
[219]

Zhongxiang Sun, Zihua Si, Xiaoxue Zang, Kai Zheng, Yang Song, Xiao Zhang, and Jun Xu. 2024. Large language models enhanced collaborative filtering. InProceedings of the 33rd ACM International Conference on Information and Knowledge Management. 2178–2188

2024
[220]

Zehua Sun, Yonghui Xu, Yong Liu, Wei He, Lanju Kong, Fangzhao Wu, Yali Jiang, and Lizhen Cui. 2024. A survey on federated recommendation systems.IEEE Transactions on Neural Networks and Learning Systems36, 1 (2024), 6–20

2024
[221]

Jiaxi Tang and Ke Wang. 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. InProceedings of the eleventh ACM international conference on web search and data mining. 565–573

2018
[222]

Antonela Tommasel. 2024. Fairness Matters: A look at LLM-generated group recommendations. InProceedings of the 18th ACM Conference on Recommender Systems. 993–998

2024
[223]

Thi Ngoc Trang Tran, Alexander Felfernig, Christoph Trattner, and Andreas Holzinger. 2021. Recommender systems in the healthcare domain: state-of-the-art and research issues.Journal of Intelligent Information Systems57, 1 (2021), 171–201

2021
[224]

Jean-Baptiste Truong, Pratyush Maini, Robert J Walls, and Nicolas Papernot. 2021. Data-free model extraction. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 4771–4780

2021
[225]

María Cora Urdaneta-Ponte, Amaia Mendez-Zorrilla, and Ibon Oleagordia-Ruiz. 2021. Recommendation systems for education: Systematic review. Electronics10, 14 (2021), 1611

2021
[226]

Bohao Wang, Jiawei Chen, Changdong Li, Sheng Zhou, Qihao Shi, Yang Gao, Yan Feng, Chun Chen, and Can Wang. 2024. Distributionally robust graph-based recommendation system. InProceedings of the ACM web conference 2024. 3777–3788

2024
[227]

Bohao Wang, Jiawei Chen, Feng Liu, Changwang Zhang, Jun Wang, Canghong Jin, Chun Chen, and Can Wang. 2026. Does LLM Focus on the Right Words? Mitigating Context Bias in LLM-based Recommenders. InProceedings of the ACM Web Conference 2026. 6688–6699

2026
[228]

Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, et al
[229]

DecodingTrust: A Comprehensive Assessment of Trustworthiness in{GPT}Models. (2023)

2023
[230]

Bohao Wang, Feng Liu, Jiawei Chen, Xingyu Lou, Changwang Zhang, Jun Wang, Yuegang Sun, Yan Feng, Chun Chen, and Can Wang. 2025. Msl: Not all tokens are what you need for tuning llm as a recommender. InProceedings of the 48th international ACM SIGIR conference on research and d...

2025
[231]

Bohao Wang, Feng Liu, Changwang Zhang, Jiawei Chen, Yudi Wu, Sheng Zhou, Xingyu Lou, Jun Wang, Yan Feng, Chun Chen, et al. 2025. Llm4dsr: Leveraging large language model for denoising sequential recommendation.ACM Transactions on Information Systems44, 1 (2025), 1–32

2025
[232]

Hangyu Wang, Jianghao Lin, Bo Chen, Yang Yang, Ruiming Tang, Weinan Zhang, and Yong Yu. 2025. Towards efficient and effective unlearning of large language models for recommendation.Frontiers of Computer Science19, 3 (2025), 193327

2025
[233]

Hanbing Wang, Xiaorui Liu, Wenqi Fan, Xiangyu Zhao, Venkataramana Kini, Devendra Yadav, Fei Wang, Zhen Wen, Jiliang Tang, and Hui Liu
[234]

Rethinking large language model architectures for sequential recommendations.arXiv preprint arXiv:2402.09543(2024)

2024
[235]

Jianling Wang, Haokai Lu, James Caverlee, Ed H Chi, and Minmin Chen. 2024. Large language models as data augmenters for cold-start item recommendation. InCompanion Proceedings of the ACM Web Conference 2024. 726–729

2024
[236]

Lei Wang, Songheng Zhang, Yun Wang, Ee-Peng Lim, and Yong Wang. 2023. LLM4Vis: Explainable Visualization Recommendation using ChatGPT. InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track. 675–692

2023
[237]

Liyuan Wang, Xingxing Zhang, Hang Su, and Jun Zhu. 2024. A comprehensive survey of continual learning: Theory, method and application.IEEE transactions on pattern analysis and machine intelligence46, 8 (2024), 5362–5383

2024
[238]

Mengru Wang, Xingyu Chen, Yue Wang, Zhiwei He, Jiahao Xu, Tian Liang, Qiuzhi Liu, Yunzhi Yao, Wenxuan Wang, Ruotian Ma, et al. 2026. Two experts are all you need for steering thinking: Reinforcing cognitive effort in moe reasoning models without additional training.Advances in...

2026
[239]

Menghan Wang, Yuchen Guo, Duanfeng Zhang, Jianian Jin, Minnie Li, Dan Schonfeld, and Shawn Zhou. 2024. Enabling Explainable Recommendation in E-commerce with LLM-powered Product Knowledge Graph.arXiv preprint arXiv:2412.01837(2024)

2024
[240]

Mengru Wang, Ziwen Xu, Shengyu Mao, Shumin Deng, Zhaopeng Tu, Huajun Chen, and Ningyu Zhang. 2025. Beyond prompt engineering: Robust behavior control in llms via steering target atoms. InProceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (V...

2025
[241]

Shijie Wang, Wenqi Fan, Yue Feng, Xinyu Ma, Shuaiqiang Wang, and Dawei Yin. 2025. Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation.arXiv preprint arXiv:2501.02226(2025)

2025
[242]

Shicheng Wang, Hengzhu Tang, Li Gao, Shu Guo, Suqi Cheng, Junfeng Wang, Dawei Yin, Tingwen Liu, and Lihong Wang. 2025. Towards S2-Challenges Underlying LLM-Based Augmentation for Personalized News Recommendation. InProceedings of the AAAI Conference on Artificial Intelligence,...

2025
[243]

Shoujin Wang, Xiuzhen Zhang, Yan Wang, and Francesco Ricci. 2024. Trustworthy recommender systems.ACM Transactions on Intelligent Systems and Technology15, 4 (2024), 1–20

2024
[244]

Shuyao Wang, Zhi Zheng, Yongduo Sui, and Hui Xiong. 2025. Unleashing the Power of Large Language Model for Denoising Recommendation. In Proceedings of the ACM on Web Conference 2025. 252–263

2025
[245]

Wenjie Wang, Fuli Feng, Xiangnan He, Liqiang Nie, and Tat-Seng Chua. 2021. Denoising implicit feedback for recommendation. InProceedings of the 14th ACM international conference on web search and data mining. 373–381

2021
[246]

Wenjie Wang, Fuli Feng, Xiangnan He, Hanwang Zhang, and Tat-Seng Chua. 2021. Clicks can be cheating: Counterfactual recommendation for mitigating clickbait issue. InProceedings of the 44th international ACM SIGIR conference on research and development in information retrieval....

2021
[247]

Xinfeng Wang, Jin Cui, Yoshimi Suzuki, and Fumiyo Fukumoto. 2024. RDRec: Rationale Distillation for LLM-based Recommendation. InProceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 65–74

2024
[248]

Xi Wang, Hossein A Rahmani, Jiqun Liu, and Emine Yilmaz. 2023. Improving conversational recommendation systems via bias analysis and language-model-enhanced data augmentation.arXiv preprint arXiv:2310.16738(2023)

2023
[249]

Yan Wang, Zhixuan Chu, Xin Ouyang, Simeng Wang, Hongyan Hao, Yue Shen, Jinjie Gu, Siqiao Xue, James Zhang, Qing Cui, et al. 2024. Llmrg: Improving recommendations through large language model reasoning graphs. InProceedings of the AAAI Conference on Artificial Intelligence, Vo...

2024
[250]

Yuyan Wang, Pan Li, and Minmin Chen. 2025. The Blessing of Reasoning: LLM-Based Contrastive Explanations in Black-Box Recommender Systems.arXiv preprint arXiv:2502.16759(2025)

2025 arXiv
[251]

Yu Wang, Zhiwei Liu, Jianguo Zhang, Weiran Yao, Shelby Heinecke, and Philip S Yu. 2023. Drdt: Dynamic reflection with divergent thinking for llm-based sequential recommendation.arXiv preprint arXiv:2312.11336(2023)

2023
[252]

Yuhao Wang, Junwei Pan, Xinhang Li, Maolin Wang, Yuan Wang, Yue Liu, Dapeng Liu, Jie Jiang, and Xiangyu Zhao. 2025. Empowering large language model for sequential recommendation via multimodal embeddings and semantic ids. InProceedings of the 34th ACM International Conference ...

2025
[253]

Yidan Wang, Zhaochun Ren, Weiwei Sun, Jiyuan Yang, Zhixiang Liang, Xin Chen, Ruobing Xie, Su Yan, Xu Zhang, Pengjie Ren, et al . 2024. Enhanced generative recommendation via content and collaboration integration.arXiv preprint arXiv:2403.18480(2024)

2024
[254]

Yuling Wang, Changxin Tian, Binbin Hu, Yanhua Yu, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, Liang Pang, and Xiao Wang. 2024. Can small language models be good reasoners for sequential recommendation?. InProceedings of the ACM Web Conference 2024. 3876–3887

2024
[255]

Zongwei Wang, Min Gao, Junliang Yu, Xinyi Gao, Quoc Viet Hung Nguyen, Shazia Sadiq, and Hongzhi Yin. 2025. Id-free not risk-free: Llm-powered agents unveil risks in id-free recommender systems. InProceedings of the 48th International ACM SIGIR Conference on Research and Develo...

2025
[256]

Zongwei Wang, Min Gao, Junliang Yu, Yupeng Hou, Shazia Sadiq, and Hongzhi Yin. 2025. RuleAgent: Discovering Rules for Recommendation Denoising with Autonomous Language Agents.arXiv preprint arXiv:2503.23374(2025)

2025
[257]

Zhefan Wang, Yuanqing Yu, Wendi Zheng, Weizhi Ma, and Min Zhang. 2024. Macrec: A multi-agent collaboration framework for recommendation. InProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2760–2764

2024
[258]

Albert Webson and Ellie Pavlick. 2022. Do prompt-based models really understand the meaning of their prompts?. InProceedings of the 2022 conference of the north american chapter of the association for computational linguistics: Human language technologies. 2300–2344

2022
[259]

Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2021. Finetuned language models are zero-shot learners.arXiv preprint arXiv:2109.01652(2021)

2021 arXiv
[260]

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022. Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems35 (2022), 24824–24837

2022
[261]

Kang Wei, Jun Li, Chuan Ma, Ming Ding, Wen Chen, Jun Wu, Meixia Tao, and H Vincent Poor. 2023. Personalized federated learning with differential privacy and convergence guarantee.IEEE Transactions on Information Forensics and Security18 (2023), 4488–4503

2023
[262]

Wei Wei, Xubin Ren, Jiabin Tang, Qinyong Wang, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, and Chao Huang. 2024. Llmrec: Large language models with graph augmentation for recommendation. InProceedings of the 17th ACM International Conference on Web Search and Data Mining. 806–815

2024
[263]

Stanisław Woźniak, Jacek Duszenko, Jan Kocoń, and Przemysaw Kazienko. 2025. Improving LLM-Based Recommender Systems with User- Controllable Profiles. InCompanion Proceedings of the ACM on Web Conference 2025. 2102–2111

2025
[264]

Junda Wu, Cheng-Chun Chang, Tong Yu, Zhankui He, Jianing Wang, Yupeng Hou, and Julian McAuley. 2024. Coral: Collaborative retrieval- augmented large language models improve long-tail recommendation. InProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Dat...

2024
[265]

Tongshuang Wu, Michael Terry, and Carrie Jun Cai. 2022. Ai chains: Transparent and controllable human-ai interaction by chaining large language model prompts. InProceedings of the 2022 CHI conference on human factors in computing systems. 1–22

2022
[266]

Xinghao Wu, Xuefeng Liu, Jianwei Niu, Haolin Wang, Shaojie Tang, and Guogang Zhu. 2024. FedLoRA: When personalized federated learning meets low-rank adaptation. (2024)

2024
[267]

Yiqing Wu, Ruobing Xie, Zhao Zhang, Yongchun Zhu, Fuzhen Zhuang, Jie Zhou, Yongjun Xu, and Qing He. 2023. Attacking pre-trained recommendation. InProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1811–1815

2023
[268]

Yu Xia, Sungchul Kim, Tong Yu, Ryan A Rossi, and Julian McAuley. 2026. Multi-agent collaborative filtering: Orchestrating users and items for agentic recommendations. InProceedings of the ACM Web Conference 2026. 8649–8652

2026
[269]

Chen Xu, Wenjie Wang, Yuxin Li, Liang Pang, Jun Xu, and Tat-Seng Chua. 2023. A study of implicit ranking unfairness in large language models. arXiv preprint arXiv:2311.07054(2023)

2023
[270]

Lanling Xu, Junjie Zhang, Bingqian Li, Jinpeng Wang, Sheng Chen, Wayne Xin Zhao, and Ji-Rong Wen. 2025. Tapping the potential of large language models as recommender systems: A comprehensive framework and empirical analysis.ACM Transactions on Knowledge Discovery from Data19, ...

2025
[271]

Wujiang Xu, Yunxiao Shi, Zujie Liang, Xuying Ning, Kai Mei, Kun Wang, Xi Zhu, Min Xu, and Yongfeng Zhang. 2025. iAgent: LLM Agent as a Shield between User and Recommender Systems.arXiv preprint arXiv:2502.14662(2025)

2025
[272]

Wujiang Xu, Qitian Wu, Zujie Liang, Jiaojiao Han, Xuying Ning, Yunxiao Shi, Wenfang Lin, and Yongfeng Zhang. 2025. SLMRec: Distilling large language models into small for sequential recommendation. InThe Thirteenth International Conference on Learning Representations

2025
[273]

Liu Yang, Ben Tan, Vincent W Zheng, Kai Chen, and Qiang Yang. 2020. Federated recommendation systems. InFederated Learning: Privacy and Incentive. Springer, 225–239

2020
[274]

Mengyuan Yang, Mengying Zhu, Yan Wang, Linxun Chen, Yilei Zhao, Xiuyuan Wang, Bing Han, Xiaolin Zheng, and Jianwei Yin. 2024. Fine-tuning large language model based explainable recommendation with explainable quality reward. InProceedings of the AAAI Conference on Artificial I...

2024
[275]

Shiyi Yang, Zhibo Hu, Xinshu Li, Chen Wang, Tong Yu, Xiwei Xu, Liming Zhu, and Lina Yao. 2025. DrunkAgent: Stealthy Memory Corruption in LLM-Powered Recommender Agents.arXiv preprint arXiv:2503.23804(2025)

2025
[276]

Weiqin Yang, Bohao Wang, Zhenxiang Xu, Jiawei Chen, Shengjia Zhang, Jingbang Chen, Canghong Jin, and Can Wang. 2026. BEAR: Towards Beam-Search-Aware Optimization for Recommendation with Large Language Models.arXiv preprint arXiv:2601.22925(2026)

2026 arXiv
[277]

Wooseong Yang, Weizhi Zhang, Yuqing Liu, Yuwei Han, Yu Wang, Junhyun Lee, and Philip S Yu. 2025. Cold-Start Recommendation with Knowledge-Guided Retrieval-Augmented Generation.arXiv preprint arXiv:2505.20773(2025)

2025
[278]

Zhengyi Yang, Xiangnan He, Jizhi Zhang, Jiancan Wu, Xin Xin, Jiawei Chen, and Xiang Wang. 2023. A generic learning framework for sequential recommendation with distribution shifts. InProceedings of the 46th International ACM SIGIR Conference on Research and Development in Info...

2023
[279]

Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R Narasimhan, and Yuan Cao. 2022. React: Synergizing reasoning and acting in language models. InThe eleventh international conference on learning representations

2022
[280]

Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha. 2018. Privacy risk in machine learning: Analyzing the connection to overfitting. In2018 IEEE 31st computer security foundations symposium (CSF). IEEE, 268–282

2018
[281]

Hongzhi Yin, Liang Qu, Tong Chen, Wei Yuan, Ruiqi Zheng, Jing Long, Xin Xia, Yuhui Shi, and Chengqi Zhang. 2024. On-device recommender systems: A comprehensive survey.arXiv preprint arXiv:2401.11441(2024)

2024
[282]

Mingzhe Yu, Yunshan Ma, Lei Wu, Changshuo Wang, Xue Li, and Lei Meng. 2025. FashionDPO: fine-tune fashion outfit generation model using direct preference optimization. InProceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retri...

2025
[283]

Qihang Yu, Kairui Fu, Shengyu Zhang, Zheqi Lv, Fan Wu, and Fei Wu. 2025. ThinkRec: Thinking-based recommendation via LLM.arXiv preprint arXiv:2505.15091(2025)

2025
[284]

Xiaohan Yu, Li Zhang, and Chong Chen. 2025. Explainable ctr prediction via llm reasoning. InProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining. 707–716

2025
[285]

Wei Yuan, Chaoqun Yang, Guanhua Ye, Tong Chen, Nguyen Quoc Viet Hung, and Hongzhi Yin. 2024. FELLAS: Enhancing Federated Sequential Recommendation with LLM as External Services.ACM Transactions on Information Systems(2024)

2024
[286]

Zheng Yuan, Fajie Yuan, Yu Song, Youhua Li, Junchen Fu, Fei Yang, Yunzhu Pan, and Yongxin Ni. 2023. Where to go next for recommender systems? id-vs. modality-based recommender models revisited. InProceedings of the 46th International ACM SIGIR Conference on Research and Develo...

2023
[287]

Zhenrui Yue, Sara Rabhi, Gabriel de Souza Pereira Moreira, Dong Wang, and Even Oldridge. 2023. Llamarec: Two-stage recommendation using large language models for ranking.arXiv preprint arXiv:2311.02089(2023)

2023
[288]

Chi Zhang, Yantong Du, Xiangyu Zhao, Qilong Han, Rui Chen, and Li Li. 2022. Hierarchical item inconsistency signal learning for sequence denoising in sequential recommendation. InProceedings of the 31st ACM international conference on information & knowledge management. 2508–2518

2022
[289]

Chi Zhang, Qilong Han, Rui Chen, Xiangyu Zhao, Peng Tang, and Hongtao Song. 2024. Ssdrec: Self-augmented sequence denoising for sequential recommendation. In2024 IEEE 40th International Conference on Data Engineering (ICDE). IEEE, 803–815. Manuscript submitted to ACM 46 Bohao ...

2024
[290]

Chunxu Zhang, Guodong Long, Hongkuan Guo, Xiao Fang, Yang Song, Zhaojie Liu, Guorui Zhou, Zijian Zhang, Yang Liu, and Bo Yang. 2024. Federated adaptation for foundation model-based recommendations.arXiv preprint arXiv:2405.04840(2024)

2024
[291]

Gangyi Zhang. 2023. User-centric conversational recommendation: Adapting the need of user with large language models. InProceedings of the 17th ACM Conference on Recommender Systems. 1349–1354

2023
[292]

Honglei Zhang, Fangyuan Luo, Jun Wu, Xiangnan He, and Yidong Li. 2023. LightFR: Lightweight federated recommendation with privacy-preserving matrix factorization.ACM Transactions on Information Systems41, 4 (2023), 1–28

2023
[293]

Jizhi Zhang, Keqin Bao, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023. Is chatgpt fair for recommendation? evaluating fairness in large language model recommendation. InProceedings of the 17th ACM Conference on Recommender Systems. 993–999

2023
[294]

Junjie Zhang, Yupeng Hou, Ruobing Xie, Wenqi Sun, Julian McAuley, Wayne Xin Zhao, Leyu Lin, and Ji-Rong Wen. 2024. Agentcf: Collaborative learning with autonomous language agents for recommender systems. InProceedings of the ACM Web Conference 2024. 3679–3689

2024
[295]

Jiaming Zhang, Yuyuan Li, Yiqun Xu, Li Zhang, Xiaohua Feng, Zhifei Ren, and Chaochao Chen. 2025. BiFair: A Fairness-aware Training Framework for LLM-enhanced Recommender Systems via Bi-level Optimization.arXiv preprint arXiv:2507.04294(2025)

2025
[296]

Jinghao Zhang, Yuting Liu, Qiang Liu, Shu Wu, Guibing Guo, and Liang Wang. 2024. Stealthy attack on large language model based recommendation. arXiv preprint arXiv:2402.14836(2024)

2024
[297]

Junjie Zhang, Ruobing Xie, Yupeng Hou, Xin Zhao, Leyu Lin, and Ji-Rong Wen. 2023. Recommendation as instruction following: A large language model empowered recommendation approach.ACM Transactions on Information Systems(2023)

2023
[298]

Kaike Zhang, Qi Cao, Fei Sun, Yunfan Wu, Shuchang Tao, Huawei Shen, and Xueqi Cheng. 2025. Robust recommender system: a survey and future directions.Comput. Surveys58, 1 (2025), 1–38

2025
[299]

Kaike Zhang, Qi Cao, Yunfan Wu, Fei Sun, Huawei Shen, and Xueqi Cheng. 2024. Lorec: Combating poisons with large language model for robust sequential recommendation. InProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retriev...

2024
[300]

Minxing Zhang, Zhaochun Ren, Zihan Wang, Pengjie Ren, Zhunmin Chen, Pengfei Hu, and Yang Zhang. 2021. Membership inference attacks against recommender systems. InProceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security. 864–879

2021

Pith tools

Reviewed June 28, 2026 · model on record in the stance chip above.

[1] [1]

Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, and Luiz Pizzato

[2] [2]

Abdollahpouri et al.User Modeling and User-Adapted Interaction30, 1 (2020), 127–158

Multistakeholder recommendation: Survey and research directions: H. Abdollahpouri et al.User Modeling and User-Adapted Interaction30, 1 (2020), 127–158

2020

[3] [3]

Hasan Abu-Rasheed, Christian Weber, and Madjid Fathi. 2024. Knowledge graphs as context sources for llm-based explanations of learning recommendations. In2024 IEEE Global Engineering Education Conference (EDUCON). IEEE, 1–5

2024

[4] [4]

Aaron Adcock, Aayushi Srivastava, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pande, Abhinav Pandey, Abhinav Sharma, Abhishek Kadian, Abhishek Kumawat, Adam Kelsey, et al. 2026. The Llama 4 Herd: Architecture, Training, Evaluation, and Deployment Notes.arXiv preprint arXiv:2601.11659(2026)

work page arXiv 2026

[5] [5]

Julien Albert, Martin Balfroid, Miriam Doh, Jérémie Bogaert, Luca La Fisca, Liesbet De Vos, Bryan Renard, Vincent Stragier, and Emmanuel Jean. 2024. User Preferences for Large Language Model versus Template-Based Explanations of Movie Recommendations: A Pilot Study.ArXiv abs/2409.06297 (2024). https://api.semanticscholar.org/CorpusID:272550316

work page arXiv 2024

[6] [6]

Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz. 2019. Invariant risk minimization.arXiv preprint arXiv:1907.02893(2019)

work page Pith review arXiv 2019

[7] [7]

Giacomo Balloccu, Ludovico Boratto, Christian Cancedda, Gianni Fenu, and Mirko Marras. 2023. Faithful Path Language Modeling for Explainable Recommendation over Knowledge Graph.arXiv preprint arXiv:2310.16452(2023)

work page arXiv 2023

[8] [8]

Yejin Bang, Ziwei Ji, Alan Schelten, Anthony Hartshorn, Tara Fowler, Cheng Zhang, Nicola Cancedda, and Pascale Fung. 2025. Hallulens: Llm hallucination benchmark. InProceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 24128–24156

2025