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REVIEW 5 major objections 6 minor 40 references

LLM-Based Re-Ranking for Real Estate Search

T0 review · 5 major / 6 minor · reviewed 2026-08-02 · deepseek-v4-flash

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Pith's one-line read This paper claims that adding an LLM point-wise re-ranking stage to a conversational real-estate search system improves ranking quality and downstream user actions, with production A/B results of +5.3% click-through rate and +4.8% scheduled

desk verdict Real production evidence, but the A/B stats are under-reported and the offline judge is too close to the re-ranker; still worth a serious referee. read the letter →

arxiv 2607.14835 v2 pith:JY24Z7U7 submitted 2026-07-16 cs.IR

Nkateko Ntimane , Rafael Guedes , Tiago Cunha , Pedro Nogueira This is my paper
classification cs.IR
keywords LLMre-rankingconversationalrecommendationrealestatesearchpoint-wiserankingLLM-as-a-JudgesyntheticquerygenerationA/Btestinguserprofile
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

Real-estate search intent is often conversational and qualitative—proximity to work, neighborhood character, trade-offs like paying more for a gym—and structured filters cannot capture it. The paper tries to show that a large language model applied only at the re-ranking stage, over a small candidate set already retrieved by a standard search engine, can absorb this conversational context and meaningfully improve results without replacing the serving stack. In a production housing marketplace, the authors compared a conversational assistant with and without the LLM re-ranker. The re-ranked version produced statistically significant gains: 5.3% more clicks and 4.8% more scheduled visits, with end-to-end latency increasing by about 4.2 seconds and inference cost by about 7%. If this is right, conversational search can improve real recommendation outcomes through a narrow, pragmatic intervention rather than an end-to-end generative rewrite.

What carries the argument

The load-bearing mechanism is point-wise affinity scoring: each candidate is evaluated independently by the LLM, which makes the re-ranking stage parallel and latency-tolerant. The scoring prompt combines the user's free-text requirement, a generated textual user profile, structured filters, property metadata, and candidate-set statistics such as price ranges and amenity prevalence. These statistics let the model judge relative value even though each property is scored alone. The user search profile—a condensed natural-language summary of behavioral history and conversational statements—is the key object that injects personalization into the scores.

What would settle it

Take a fresh sample of production traces, have multiple human raters label relevance for query–listing pairs without knowing which order came from the re-ranker, and recompute the offline ranking metrics; if the re-ranker's nDCG advantage over the baseline shrinks to zero or reverses, the central claim fails. A complementary check: run a longer production A/B that tracks completed visits or signed leases rather than clicks and scheduled visits.

Watch

Extended reading notes

Core claim

The discovery is that a point-wise LLM scorer, given a natural-language user profile and aggregate statistics over the candidate set, can reorder retrieved housing listings so that they better match the user's actual goals. The profile is assembled from previous sessions, the current conversation, and historical interaction signals, with the most recent and explicit conversational statements given priority. Each candidate property receives an affinity score in [0,1] that combines hard-constraint satisfaction, preference alignment, and relative value; candidates are sorted by that score, with ties broken by the original retrieval score. On an offline set of 960,000 query–listing pairs built f

Load-bearing premise

The offline evaluation treats an LLM judge, validated on only 1,000 human-labeled pairs (96% agreement), as ground truth for 960,000 query–listing pairs; if that judge shares the re-ranker's systematic biases, the offline gains are inflated.

Editorial extensions

If this is right

  • LLM re-ranking can be layered on top of existing retrieval systems and improve engagement without end-to-end generative retrieval or a full rewrite of the serving stack.
  • Candidate-set statistics add consistent, if modest, ranking gains in point-wise scoring, suggesting that even independent scoring benefits from aggregate context.
  • House descriptions and textual user profiles contribute the largest semantic gains; explicit scoring-calibration instructions actually hurt slightly, so prompt design matters more than rigid rules.
  • The deployed system did not yet include the textual user profile, so the production A/B may understate the approach's full potential.
  • Synthetic query generation mixed with production queries and LLM-as-a-Judge labeling offers a scalable template for offline evaluation in conversational search.

Reading between the lines

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

  • A natural extension would be to track whether the +4.8% scheduled-visit gain persists further downstream, in completed tours, lease applications, or signed contracts—the strongest evidence that re-ranking improves real outcomes rather than engagement.
  • The judge win rate on sampled production traces is very high (95%), which invites a skeptical check: a blind human preference study on the same traces would separate style effects from genuine ranking improvements.
  • The paper does not examine fairness across neighborhoods or demographic groups; a plausible caution is that an LLM profile re-ranker could amplify existing spatial or socioeconomic biases in housing search.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 6 minor

Summary. The paper describes a production conversational real-estate search assistant (Concierge) at QuintoAndar and proposes an LLM-based point-wise re-ranker. The re-ranker scores each retrieved property using the user's conversational request, an LLM-generated user profile, structured filters, property metadata/descriptions, and aggregate candidate-set statistics, then sorts candidates by the resulting affinity score. The authors build a proprietary 960,000 query-item offline dataset from synthetic and production queries with LLM-as-a-Judge relevance labels, evaluate the re-ranker offline against a retrieval baseline, and report a production A/B test with claimed statistically significant improvements of +5.3% in CTR and +4.8% in scheduled visits. The paper concludes that LLM re-ranking improves ranking quality and downstream engagement while remaining operationally feasible.

Significance. If fully verified, this would be a valuable industrial case study: it demonstrates that an LLM point-wise re-ranker can improve real-estate search in production without replacing the retrieval stack, and it provides a large-scale offline benchmark plus an independent online experiment. The production A/B test is the strongest positive evidence, and the reporting of latency and cost overhead is useful. However, the current reporting of the A/B test lacks the statistical detail needed to verify the central claim, and the offline and LLM-judge evaluations rely heavily on LLM-generated judgments with only small human validation. The contribution is therefore significant but conditional on additional evidence.

major comments (5)
  1. [§4.4 (A/B test, Table-related text)] The abstract's central claim of 'statistically significant' production gains rests entirely on this A/B test, but the statistical reporting is incomplete. The paper reports only relative uplifts (+5.3% CTR, +4.8% scheduled visits) and states significance without giving p-values, confidence intervals, the test procedure, per-arm sample sizes, or the unit of randomization/analysis. With 'more than 200,000 production recommendation traces,' if traces rather than users are the unit of analysis, user-level correlation (pseudoreplication) could inflate significance. Please report a user-level clustered analysis, exact denominators and definitions of the metrics, and a pre-specified testing plan.
  2. [§4.1.3 and §4.1.5 (LLM-as-a-Judge labels)] The offline benchmark's 960,000 labels are generated by Claude Sonnet 4 with human validation on only 1,000 pairs (96% agreement). Since the evaluated re-ranker is itself an LLM and the labels are also LLM judgments, shared systematic biases (e.g., preference for fluent listing text or model-familiar patterns) could inflate offline gains. The 1,000-pair validation is not stratified across the six synthetic query types, hard negatives, or production-query segments, so it is unclear whether 96% agreement extends to the most difficult pairs. Please provide agreement by query type/candidate difficulty and report judge self-consistency.
  3. [§4.5 (LLM-as-a-Judge win rate)] The judge-based evaluation reports that the re-ranked list was preferred over the baseline in 95% of 3,944 production traces, with no human validation, no confidence intervals, and no checks for judge bias or position effects. A 95% pairwise win rate against a strong retrieval baseline is implausibly high and suggests possible shared bias between the judge and the re-ranker. Please report inter-annotator agreement (human vs. judge), judge sensitivity to list order/format, and calibrate the judge on a human-annotated pairwise sample.
  4. [§4.3, Table 1 (offline significance)] The offline results mark configurations as statistically significant (p<0.05) but no statistical test, variance estimate, confidence interval, or number of independent query/item groups is reported. Several improvements are small (0.8%, 0.9%, 1.1%), and the candidate-set construction includes anchor items that may be trivially relevant, making it unclear whether the differences are beyond sampling noise. Please report query-level paired tests with confidence intervals, and clarify whether the test accounts for repeated items across groups.
  5. [§4.4 (latency and confound)] The treatment arm adds an average 4.2s end-to-end latency. The observed CTR and scheduled-visit uplifts could partly reflect a differential user experience (e.g., slower but more 'deliberative' interaction) rather than ranking quality alone. No guardrail metrics are reported—abandonment, post-click satisfaction, repeat use, or visit show rate—so this confound cannot be ruled out. Please report guardrail metrics and, if feasible, a latency-matched control or a sensitivity analysis.
minor comments (6)
  1. [§3.3.2 (candidate-set statistics)] The aggregate statistics are described only qualitatively; please specify how many candidates are used, which statistics are computed, and how they are serialized into the prompt, so the method is reproducible.
  2. [§3.2 (user search profile)] The behavioral profile 'interaction weights' are mentioned but not defined. Please give the actual weighting scheme or a reference, since this is a component of the proposed method.
  3. [§4.1.1 and §4.1.3 (human validation samples)] Two separate 1,000-pair human evaluations are described (one for synthetic query quality, one for relevance labels). Please clarify whether the samples overlap and report the exact annotation instructions and annotator background.
  4. [§4.4 (Figure 6)] The CTR-over-time figure would benefit from confidence bands and explicit arm sizes per day; currently the visual comparison is hard to interpret without uncertainty information.
  5. [§4.5 (Figure 7)] The daily win-rate figure should show evaluation volume and confidence intervals; daily rates with very small volumes can be misleading.
  6. [§4.4 (baseline description)] Please describe the baseline retrieval model and ranking function in enough detail (or cite an internal reference) so readers can assess the strength of the baseline being compared against.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the re-ranker is not fitted to the LLM-judge labels, and the production A/B test is an independent behavioral measurement.

full rationale

After walking the derivation chain, I find no step that reduces a predicted quantity to an input by construction. Offline labels (§4.1.3) are assigned by Claude Sonnet 4 under a structured constraint-checking protocol, with two human agreement checks (94% on query generation, 96% on relevance labels). These labels are not generated by the re-ranker, and no parameter of the re-ranker is fit to them; the LLM re-ranker uses a fixed prompt (Figure 3), not trained weights, so the offline nDCG/Recall improvements are not statistically forced. The online A/B test (§4.4) is an independent behavioral measurement on >200,000 production traces, measuring CTR and scheduled visits, not the LLM judge's preferences; while its statistical reporting is incomplete (no confidence intervals, p-values, or clustering details), incompleteness is a reproducibility/correctness concern rather than circularity. The §4.5 LLM-judge preference test is also an external judge (not the re-ranker itself), and the 95% win rate is a comparison, not a derivation from the re-ranker's own outputs. The paper contains no self-citations and imports no uniqueness theorem from the authors' prior work. The legitimate worry about shared bias between LLM judges and LLM rankers is a validity threat for the offline and judge-based evaluations, not a definitional reduction of the kind required to establish circularity. The central production claim rests on the A/B test, which is external to the paper's fitted or annotated inputs. Therefore no circular step is exhibited, and the score is 0.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central claims rest on hand-chosen experimental choices (k values, interaction weights) and on assumptions that LLM-generated labels and synthetic queries faithfully represent real user relevance. No new theoretical entities are introduced; the main non-reproducibility is the proprietary dataset.

free parameters (3)
  • candidate pool size k = 10
    Chosen for building hard negatives in the offline dataset; affects label distribution and reported ranking metrics (§4.1.2).
  • top-k surfaced to user = 5
    Production system returns only top-5 properties; determines Recall@5 and nDCG@5 reported offline (§3.1, §4.3).
  • behavioral profile interaction weights
    Clicks treated as weak signals, favorites and scheduled visits as stronger signals; numerical weights are not reported (§3.2).
assumptions (5)
  • domain assumption LLM-as-a-Judge labels validated on 1,000 pairs generalize to 960,000 pairs
    Used to build offline ground truth; if judge bias is systematic, offline metrics are inflated. §4.1.3.
  • ad hoc to paper LLM-generated synthetic queries approximate realistic conversational search behavior
    Synthetic queries are generated by an LLM from anchor listings; distribution may not match production intent. §4.1.1.
  • domain assumption CTR and scheduled visits are reliable proxies for ranking quality in production
    Used as primary A/B outcome metrics; they can be affected by presentation order, trust, or external factors. §4.4.
  • domain assumption Treatment and control groups are comparable in the A/B test
    50/50 random assignment is assumed to control confounds; no pre-experiment balance check is reported. §4.4.
  • domain assumption Dense embedding similarity (e5-base + FAISS) yields meaningful hard negatives
    Offline candidate pools rely on this retrieval for both synthetic and production queries; if embeddings miss semantic similarity, the label distribution changes. §4.1.2.

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Cite this review

Pith. "Pith review of LLM-Based Re-Ranking for Real Estate Search." pith.science (2026). https://pith.science/paper/JY24Z7U7

@misc{pith2026260714835,
  author       = {Pith},
  title        = {Pith review of: LLM-Based Re-Ranking for Real Estate Search},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JY24Z7U7}},
  note         = {Machine review of arXiv:2607.14835}
}
read the original abstract

QuintoAndar Group operates the leading housing marketplace in Latin America for both rentals and sales. The platform replaces traditionally paper-heavy workflows with a fully digital experience, making housing transactions faster and more accessible to tenants, buyers, and landlords in the region. Finding the ideal home in such a vast catalog is inherently difficult. At the same time, the widespread adoption of conversational assistants is reshaping user expectations: people increasingly want to express their needs through open, multi-turn dialog rather than rigid filter menus and faceted search. This shift is particularly pronounced in housing, where intent is multi-dimensional, context-dependent, and rarely reducible to a small set of structured constraints. To meet these expectations, we propose a Large Language Model (LLM) based re-ranker that augments a conversational recommendation system by reordering retrieved candidates according to the nuanced, context-rich intent expressed across the user's conversation. We additionally construct a large-scale offline evaluation dataset for conversational real-estate search, containing 960,000 query-item pairs constructed from both synthetic and production queries and annotated using an LLM-as-a-Judge framework with human validation. We validate our approach both offline, on this proprietary dataset, and online, through a production A/B test. Both evaluations show consistent improvements in ranking quality, including a statistically significant increase in production of +5.3% in click-through rate and +4.8% in scheduled visits, demonstrating the value of integrating conversational context into housing recommendations.

Figures

Figures reproduced from arXiv: 2607.14835 by the authors.

Figure 1
Figure 1. Example of a structured user profile used for per [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Overview of the LLM re-ranker pipeline. 3.3.1 Point-wise Ranking Strategy. Under this strategy, each can￾didate property is scored independently with an affinity score: a scalar estimate of how well the property matches the user’s intent and preferences. This independent scoring allows candidates to be evaluated in parallel, helping keep re-ranking latency low. In contrast, list-wise methods evaluate the full candid… view at source ↗
Figure 3
Figure 3. Simplified affinity score generation prompt used [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Overview of the dataset construction pipeline. (1) Property data is sampled from the data lake. (2) Candidate listings [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 6
Figure 6. Figure 6: CTR Over Time by Ranking Type during the A/B [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: LLM-as-a-Judge win rate and evaluation volume [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]

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Reference graph

Works this paper leans on

40 extracted references · 8 linked inside Pith

  1. [1]

    Anthropic. 2025. Introducing Claude 4. https://www.anthropic.com/news/claude- 4

    2025

  2. [2]

    Honghui Bao, Wenjie Wang, Xinyu Lin, Fengbin Zhu, Teng Sun, Fuli Feng, and Tat-Seng Chua. 2025. Heterogeneous User Modeling for LLM-based Recommen- dation. InProceedings of the Nineteenth ACM Conference on Recommender Systems. Association for Computing Machinery, 145–154. LLM-Based Re-Ranking for Real Estate Search RecSys ’26, September 27– October 1, 202...

    2025

  3. [3]

    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. Association for Computing Machinery, 1007–1014

    2023

  4. [4]

    Yung-Sung Chuang, Wei Fang, Shang-Wen Li, Wen-Tau Yih, and James Glass

  5. [5]

    Sunhao Dai, Ninglu Shao, Haiyuan Zhao, Weijie Yu, Zihua Si, Chen Xu, Zhongx- iang Sun, Xiao Zhang, and Jun Xu. 2023. Uncovering ChatGPT’s Capabilities in Recommender Systems. InProceedings of the 17th ACM Conference on Recom- mender Systems. Association for Computing Machinery, 1126–1132

    2023

  6. [6]

    Matthijs Douze, Alexandr Guzhva, Chengqi Deng, Jeff Johnson, Gergely Szilvasy, Pierre-Emmanuel Mazaré, Maria Lomeli, Lucas Hosseini, and Hervé Jégou. 2024. The Faiss library. arXiv:2401.08281

    arXiv 2024

  7. [7]

    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:2305.07961

    arXiv 2023

  8. [8]

    Jingtong Gao, Bo Chen, Xiangyu Zhao, Weiwen Liu, Xiangyang Li, Yichao Wang, Wanyu Wang, Huifeng Guo, and Ruiming Tang. 2025. LLM4Rerank: LLM-based Auto-Reranking Framework for Recommendations. InProceedings of the ACM on Web Conference 2025. Association for Computing Machinery, 228–239

    2025

Show all 40 references
  1. [9]

    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. Association for Computin...

    2022

  2. [10]

    Google. 2026. Gemini 3.1 Flash-Lite. https://deepmind.google/en/models/gemini/ flash-lite/ Accessed: 2026-05-15

    2026

  3. [11]

    Hammond, Robin Burke, and Kathryn Schmitt

    Kristian J. Hammond, Robin Burke, and Kathryn Schmitt. 1994. A Case-Based Approach to knowledge Navigation. InAAAI’94

    1994

  4. [12]

    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. InAdvances in Information Retrieval: 46th European Conference on Information Retrieval. Springer-Verlag, 364–381

    2024

  5. [13]

    Xu Huang, Jianxun Lian, Yuxuan Lei, Jing Yao, Defu Lian, and Xing Xie. 2025. Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations.ACM Trans. Inf. Syst.43 (6 2025). Issue 4

    2025

  6. [14]

    Dietmar Jannach, Ahtsham Manzoor, Wanling Cai, and Li Chen. 2021. A Survey on Conversational Recommender Systems.ACM Comput. Surv.54, 5, Article 105 (May 2021), 36 pages

    2021

  7. [15]

    2026.How will AI reshape the news in 2026? Forecasts by 17 experts from around the world

    Ada Jušić and Eleonora Lima. 2026.How will AI reshape the news in 2026? Forecasts by 17 experts from around the world. Reuters Institute for the Study of Journalism, University of Oxford. https://reutersinstitute.politics.ox.ac.uk/news/how-will- ai-reshape-news-2026-forecasts-...

    2026

  8. [16]

    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

  9. [17]

    Jiahao Liu, Xueshuo Yan, Dongsheng Li, Guangping Zhang, Hansu Gu, Peng Zhang, Tun Lu, Li Shang, and Ning Gu. 2025. Improving LLM-powered Recom- mendations with Personalized Information. InProceedings of the 48th International ACM SIGIR Conference on Research and Development in...

    2025

  10. [18]

    Peng Liu, Lemei Zhang, and Jon Atle Gulla. 2023. Pre-train, Prompt, and Recom- mendation: A Comprehensive Survey of Language Modeling Paradigm Adapta- tions in Recommender Systems.Transactions of the Association for Computational Linguistics11 (2023), 1553–1571

    2023

  11. [19]

    Qidong Liu, Xiangyu Zhao, Yuhao Wang, Yejing Wang, Zijian Zhang, Yuqi Sun, Xiang Li, Maolin Wang, Pengyue Jia, Chong Chen, Wei Huang, and Feng Tian

  12. [20]

    Sichun Luo, Bowei He, Haohan Zhao, Wei Shao, Yanlin Qi, Yinya Huang, Aojun Zhou, Yuxuan Yao, Zongpeng Li, Yuanzhang Xiao, Mingjie Zhan, and Linqi Song

  13. [21]

    Xinbei Ma, Yeyun Gong, Pengcheng He, Hai Zhao, and Nan Duan. 2023. Query Rewriting in Retrieval-Augmented Large Language Models. InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 5303–5315

    2023

  14. [22]

    Marco De Nadai, Francesco Fabbri, Paul Gigioli, Alice Wang, Ang Li, Fabrizio Silvestri, Laura Kim, Shawn Lin, Vladan Radosavljevic, Sandeep Ghael, David Nyhan, Hugues Bouchard, Mounia Lalmas, and Andreas Damianou. 2024. Person- alized Audiobook Recommendations at Spotify Throu...

    2024

  15. [23]

    RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k Recommendation.ACM Trans. Inf. Syst.43 (7 2025). Issue 5

    2025

  16. [24]

    OpenAI. 2024. GPT-4o Model. https://platform.openai.com/docs/models/gpt-4o Model version: gpt-4o-2024-11-20

    2024

  17. [25]

    Enrico Palumbo, Marcus Isaksson, Alexandre Tamborrino, Maria Movin, Catalin Dincu, Ali Vardasbi, Lev Nikeshkin, Oksana Gorobets, Anders Nyman, Poppy Newdick, Hugues Bouchard, Paul Bennett, Mounia Lalmas, Dani Doro, Chris- tine Doig Cardet, and Ziad Sultan. 2025. You Say Search...

    2025

  18. [26]

    Rodrigo Nogueira, Wei Yang, Jimmy Lin, and Kyunghyun Cho. 2019. Document Expansion by Query Prediction. arXiv:1904.08375

    2019 arXiv

  19. [27]

    Francesco Ricci and Quang Nhat Nguyen. 2007. Acquiring and Revising Prefer- ences in a Critique-Based Mobile Recommender System.IEEE Intelligent Systems 22, 3 (May 2007), 22–29

    2007

  20. [28]

    Wentao Shi, Xiangnan He, Yang Zhang, Chongming Gao, Xinyue Li, Jizhi Zhang, Qifan Wang, and Fuli Feng. 2024. Large Language Models are Learnable Planners for Long-Term Recommendation. InProceedings of the 47th International ACM SI- GIR Conference on Research and Development in...

    2024

  21. [29]

    Enrico Palumbo, Gustavo Penha, Alva Liu, Marcus Eltscheminov, Jefferson Car- valho dos Santos, Alice Wang, Hugues Bouchard, Humberto Jesús Corona Pampin, and Michelle Tran Luu. 2025. AudioBoost: Increasing Audiobook Retrievability in Spotify Search with Synthetic Query Generat...

    2025 arXiv

  22. [30]

    Jianling Wang, Ainur Yessenalina, and Roshan-Ghias Alireza. 2022. Exploring Heterogeneous Metadata for Video Recommendation with Two-tower Model. (2022). arXiv:2109.11059

    2022 arXiv

  23. [31]

    Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. 2022. Text Embeddings by Weakly-Supervised Contrastive Pre-training. arXiv:2212.03533

    2022 arXiv

  24. [32]

    Yueming Sun and Yi Zhang. 2018. Conversational Recommender System. InThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR ’18). Association for Computing Machinery, New York, NY, USA, 235–244

    2018

  25. [33]

    Jiaqi Xi, Raghav Saboo, Luming Chen, Martin Wang, and Sudeep Das. 2026. Mine and Refine: Optimizing Graded Relevance in E-commerce Search Retrieval. arXiv:2602.17654

    2026

  26. [34]

    Adam Yang, Gustavo Penha, Enrico Palumbo, and Hugues Bouchard. 2025. Aligned Query Expansion: Efficient Query Expansion for Information Retrieval through LLM Alignment. (2025). arXiv:2507.11042

    2025 arXiv

  27. [35]

    Yancheng Wang, Ziyan Jiang, Zheng Chen, Fan Yang, Yingxue Zhou, Eunah Cho, Xing Fan, Yanbin Lu, Xiaojiang Huang, and Yingzhen Yang. 2024. RecMind: Large Language Model Powered Agent For Recommendation. InFindings of the Associ- ation for Computational Linguistics: NAACL 2024. ...

    2024

  28. [36]

    Joyce Zhou, Yijia Dai, and Thorsten Joachims. 2024. Language-Based User Profiles for Recommendation. arXiv:2402.15623

    2024 arXiv

  29. [37]

    Jason Zhu, Yanling Cui, Yuming Liu, Hao Sun, Xue Li, Markus Pelger, Tianqi Yang, Liangjie Zhang, Ruofei Zhang, and Huasha Zhao. 2021. TextGNN: Improving Text Encoder via Graph Neural Network in Sponsored Search. InProceedings of the Web Conference 2021. Association for Computi...

    2021

  30. [38]

    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. Association for Computing M...

    2024

  31. [2023]

    InFindings of the Association for Computational Linguistics: ACL 2023

    Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answering. InFindings of the Association for Computational Linguistics: ACL 2023. Association for Computational Linguistics, 12131–12147

    2023

  32. [2025]

    InProceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2

    Large Language Model Enhanced Recommender Systems: Methods, Ap- plications and Trends. InProceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2. Association for Computing Machinery, 6096–6106

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Reviewed August 2, 2026 · model on record in the stance chip above.