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REVIEW 3 major objections 5 minor 51 references

NExT-Search: Rebuilding User Feedback Ecosystem for Generative AI Search

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Restore the feedback loop AI search lost

desk verdict A clear, honest vision paper that diagnoses a real problem and proposes a sensible two-mode feedback architecture, but every central effectiveness claim is explicitly untested. read the letter →

arxiv 2505.14680 v1 pith:T5SLAQI2 submitted 2025-05-20 cs.IR cs.AIcs.CLcs.HC

Sunhao Dai , Wenjie Wang , Liang Pang , Jun Xu , See-Kiong Ng , Ji-Rong Wen , Tat-Seng Chua This is my paper
classification cs.IRcs.AIcs.CLcs.HC
keywords generativeAIsearchuserfeedbackloopdisconnectquerydecompositiondebugmodeshadowonlineadaptationofflineupdate
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

The paper argues that generative AI search has broken the feedback-driven improvement loop that made traditional Web search successful, because users can usually only rate the final answer rather than the intermediate steps. It proposes NExT-Search, a paradigm that reintroduces fine-grained, stage-level feedback through two modes: an active User Debug Mode and a passive Shadow User Mode. If the paradigm works, search systems could again learn continuously from user interactions, refining query decomposition, retrieval, and answer generation from real corrections. The paper is a vision paper: it lays out the architecture and the learning loops, and leaves empirical validation and system implementation to future work.

What carries the argument

The central machinery is a stage-level feedback schema attached to the standard retrieval-augmented generation pipeline: query decomposition, retrieval and ranking, and answer generation. Each stage has a defined set of debug actions—adding, removing, or reordering sub-queries; annotating relevance; re-ranking documents; applying filters; correcting facts; editing content; adjusting style—so that user corrections are attributable to a specific component. Two feedback channels produce these signals: User Debug Mode supplies explicit high-fidelity corrections, and Shadow User Mode supplies AI-generated pseudo-feedback from a learned user profile. The signals then drive online adaptation and offline fine-tuning of each stage's models.

What would settle it

A controlled deployment would settle it: give one group of users debug panels at each search stage and another group only final-answer ratings, then compare per-stage correction quality and downstream model gains. If correction rates are negligible, or if offline updates trained on debug logs do not improve retrieval or generation accuracy beyond the baseline, the central claim fails.

Watch

Extended reading notes

Core claim

The central claim is that the 'feedback loop disconnect' is the key obstacle to generative AI search's long-term improvement, and that the disconnect can be repaired by collecting feedback at each pipeline stage instead of only at the final answer. NExT-Search does this with User Debug Mode, where engaged users add or remove sub-queries, re-rank retrieved documents, and correct or restyle generated answers; and Shadow User Mode, where a personalized agent simulates user preferences and proposes edits for confirmation. The collected signals feed two loops: online adaptation, which re-runs downstream stages in the current session, and offline update, which turns corrected sub-queries, relevance annotations, and answer edits into training data for each module. The paper maintains that this restores the data flywheel that powered traditional search.

Load-bearing premise

The whole improvement loop depends on users actually giving meaningful stage-level corrections, and on the personalized agent's simulated feedback faithfully matching real user preferences; if either fails, the collected signals are too sparse or too noisy to train on.

Editorial extensions

If this is right

  • If users debug query decomposition, the corrections become direct training pairs for the decomposition model, so the system learns which sub-queries users actually need.
  • Relevance annotations and re-ranking actions provide document-level supervision, recovering the kind of fine-grained feedback that powered traditional ranking models.
  • Factual corrections and style edits in the generated answer can be used to fine-tune the generator, for instance through reinforcement learning from human feedback.
  • The combined online and offline loops let a single session be repaired in real time while long-term logs accumulate for periodic model updates.
  • The proposed feedback store could turn debugging templates into a marketplace, giving users a tangible incentive to keep contributing.

Reading between the lines

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

  • A natural extension is to treat expert users' debug traces as a reusable curriculum for the search pipeline, so that later users inherit validated decompositions and answer edits without redoing the work.
  • The Shadow User agent's fidelity is the crux of the whole scheme: if simulated feedback systematically reflects the model's own biases rather than real user preferences, the offline update loop could reinforce blind spots instead of correcting them.
  • One testable extension would be to compare system improvement rates under three conditions—explicit debug only, shadow mode only, and mixed—using edit-acceptance rates as a proxy for feedback quality.
  • The stage-level feedback schema could transfer to other retrieval-augmented systems, such as customer-support chatbots or code assistants, wherever pipeline-stage attribution is hard.
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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

3 major / 5 minor

Summary. The paper diagnoses a 'feedback loop disconnect' in generative AI search: unlike traditional Web search, where document-level implicit feedback (clicks, dwell time) directly improves ranking models, generative search pipelines (query decomposition, retrieval, answer generation) receive only coarse final-answer feedback. To close this gap, the authors propose NExT-Search, a vision for a feedback-rich ecosystem built on two modes: User Debug Mode, where users intervene at each pipeline stage, and Shadow User Mode, where a personalized LLM agent simulates feedback for less interactive users. The paper further sketches online adaptation, offline model updates, and a Feedback Store incentive mechanism. It is explicitly a perspective piece, with no system, experiments, or data; Section 6 states that empirical validation is left to future work.

Significance. Conditional on its assumptions, the paper identifies a real and timely problem: current generative search engines lack stage-level supervision, which impedes continuous improvement. The proposed paradigm is conceptually coherent and more concrete than most vision papers, offering specific interaction examples, training strategies, and a monetization mechanism. The paper also honestly acknowledges its key open challenges, especially the fidelity of user simulation and the noise in user-provided feedback. As a position paper, it is a useful agenda-setting contribution, though its central effectiveness claims remain unvalidated.

major comments (3)
  1. [Section 4.2] The offline update rationale treats user edits as gold labels: 'treating the latter as positive examples and the former as negative examples' for query decomposition, and similarly for retrieval and generation. This assumes that user corrections are always correct and the original system outputs are always wrong. In practice, user edits can be mistaken, idiosyncratic, or adversarial, and aggregating such signals without quality control could inject systematic bias into the learned models. The paper should discuss mechanisms to detect and downweight unreliable feedback, for example through cross-user agreement, edit verification, or noise-robust training objectives.
  2. [Section 3.3 and Section 5] The entire Shadow User Mode relies on a personalized user agent that 'faithfully reflects user preferences,' yet the paper provides only prompt templates and a list of future research topics. Given that this is the load-bearing assumption for scaling feedback collection, the paper should offer a concrete validation strategy, such as agreement rates between agent-simulated feedback and actual user corrections, or a protocol for A/B testing the agent's impact on downstream model improvement. Without such a proposal, the claim that this mode 'provides AI-assisted feedback for less interactive users' remains an untested assertion.
  3. [Section 3.5] The Feedback Store incentivizes users to 'receive direct financial compensation when others adopt their solutions.' This introduces a potential gaming problem: users may generate low-quality or generic templates to maximize downloads, or may misrepresent their debugging processes to increase perceived value. The paper does not address how the platform would ensure feedback quality, prevent spam, or handle disputes. At a minimum, the discussion should acknowledge these incentive risks and outline possible safeguards (e.g., reputation systems, quality verification, or platform-mediated review).
minor comments (5)
  1. [Abstract and Section 1] There are several spacing and typographical errors: 'afeedback loop disconnect' in the abstract should be 'a feedback loop disconnect'; 'anactiveUser Debug Mode' in Section 1 should be 'an active User Debug Mode'; 'online adaption' in Section 3.2 should be 'online adaptation' for consistency.
  2. [Section 2.3] The comparison bullet 'Potential for End-to-End Solutions' is presented as a contrast, but the 'risk of hallucinated content' is not unique to generative search; traditional search snippets can also mislead. Consider framing this more carefully or citing evidence on hallucination rates in generative search.
  3. [Figure 3] Figure 3 is very small and not referenced in the main text beyond the caption. The two mechanisms (online adaptation and offline update) are described in Section 4, but the figure adds little; either expand it to show the feedback flow, or consider removing it.
  4. [Related Work] The paper does not discuss existing partial feedback mechanisms in commercial generative search engines, such as Perplexity's 'related searches' or 'focus' features, or Bing Copilot's follow-up prompts. A short paragraph on how NExT-Search differs from these would strengthen the novelty claim.
  5. [Section 6] The sentence 'Due to the lack of publicly available datasets, we leave empirical validation and system implementation to future work' is a reasonable limitation, but the lack of datasets alone does not prevent a small-scale user study or simulation-based demonstration. The paper would benefit from acknowledging this choice more explicitly.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: NExT-Search is a perspective paper with no formal derivation, no fitted predictions, and no load-bearing self-citation chain.

full rationale

NExT-Search is a vision/position paper rather than a derivation. Its central claim—that generative AI search loses the fine-grained feedback loop of traditional web search and that reintroducing stage-level feedback could sustain improvement—is argued by qualitative comparison with traditional search, not by equations or fitted parameters. There is no quantity predicted from data, no parameter fitted to a subset and later called a prediction, no uniqueness theorem imported from the authors' prior work, and no ansatz smuggled in via citation. The self-citations present in the reference list (e.g., the authors' survey and bias-related works) are ordinary literature pointers and are not load-bearing for the proposed mechanism. The paper explicitly states, 'Due to the lack of publicly available datasets, we leave empirical validation and system implementation to future work,' which is a feasibility limitation rather than circularity. The weakest premise—that users will provide useful debugging feedback and that simulated feedback faithfully represents user preferences—is acknowledged in Section 5 as an open challenge; that is an unvalidated assumption, not a conclusion that reduces to its own input. No circular step can be exhibited with quotes, so a score of 0 is appropriate.

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

The paradigm rests on five untested domain assumptions: the modular structure of GenAI search, the causal role of feedback loops in Web search success, the availability and usefulness of process-level feedback, user willingness to debug, and the fidelity of LLM-simulated user feedback. There are no fitted parameters or invented physical entities, but the three designed components (paradigm, agent, store) are new proposals without independent evidence.

assumptions (5)
  • domain assumption Generative AI search uses a pipeline of query decomposition, retrieval, and answer generation.
    Section 2.2 describes this as the representative pipeline; actual systems like Perplexity or Copilot vary and may not expose such clean stages.
  • domain assumption The success of traditional Web search is driven by its fine-grained document-level feedback loop.
    Section 2.1 argues this based on refs [1,20,21,23]; it is a common narrative in IR, but the causal claim that the feedback loop is the primary driver is not proven in the paper.
  • ad hoc to paper Fine-grained, process-level feedback can be attributed to specific pipeline components and used to improve them.
    The entire NExT-Search proposal rests on this premise. Section 4 assumes corrections map cleanly to the right stage, but attribution of errors in a pipeline is a known open problem, especially for noise from the LLM generation stage.
  • ad hoc to paper Users will engage in pipeline debugging when incentivized (e.g., by the feedback store).
    Section 3.5 proposes a marketplace; user willingness to invest effort in debugging is an empirical question that is never tested. The paper even notes the interaction complexity in Section 3.4.
  • ad hoc to paper LLM-based personalized user agents can generate feedback that faithfully reflects user preferences.
    Section 3.3 assumes the agent's simulated feedback is reliable and useful; Section 5 explicitly lists building such agents as a critical challenge for future work, meaning it is an unsupported assumption in this paper.
invented entities (3)
  • NExT-Search paradigm
    purpose: Overall framework combining user debug mode and shadow user mode to collect process-level feedback for generative AI search.
    The central proposal of the paper, with no implementation or external validation.
  • Personalized user agent (Shadow User Mode)
    purpose: Simulates user behavior and generates AI-assisted feedback for users who prefer minimal interaction.
    Proposed in Section 3.3; no prototype, fidelity measurement, or validation is provided.
  • Feedback store
    purpose: A marketplace where users can share and monetize debugging templates, incentivizing participation.
    Proposed in Section 3.5; no evidence that users would participate, that templates would generalize, or that the marketplace would be sustainable.

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Cite this review

Pith. "Pith review of NExT-Search: Rebuilding User Feedback Ecosystem for Generative AI Search." pith.science (2026). https://pith.science/paper/T5SLAQI2

@misc{pith2026250514680,
  author       = {Pith},
  title        = {Pith review of: NExT-Search: Rebuilding User Feedback Ecosystem for Generative AI Search},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/T5SLAQI2}},
  note         = {Machine review of arXiv:2505.14680}
}
read the original abstract

Generative AI search is reshaping information retrieval by offering end-to-end answers to complex queries, reducing users' reliance on manually browsing and summarizing multiple web pages. However, while this paradigm enhances convenience, it disrupts the feedback-driven improvement loop that has historically powered the evolution of traditional Web search. Web search can continuously improve their ranking models by collecting large-scale, fine-grained user feedback (e.g., clicks, dwell time) at the document level. In contrast, generative AI search operates through a much longer search pipeline, spanning query decomposition, document retrieval, and answer generation, yet typically receives only coarse-grained feedback on the final answer. This introduces a feedback loop disconnect, where user feedback for the final output cannot be effectively mapped back to specific system components, making it difficult to improve each intermediate stage and sustain the feedback loop. In this paper, we envision NExT-Search, a next-generation paradigm designed to reintroduce fine-grained, process-level feedback into generative AI search. NExT-Search integrates two complementary modes: User Debug Mode, which allows engaged users to intervene at key stages; and Shadow User Mode, where a personalized user agent simulates user preferences and provides AI-assisted feedback for less interactive users. Furthermore, we envision how these feedback signals can be leveraged through online adaptation, which refines current search outputs in real-time, and offline update, which aggregates interaction logs to periodically fine-tune query decomposition, retrieval, and generation models. By restoring human control over key stages of the generative AI search pipeline, we believe NExT-Search offers a promising direction for building feedback-rich AI search systems that can evolve continuously alongside human feedback.

Figures

Figures reproduced from arXiv: 2505.14680 by the authors.

Figure 1
Figure 1. Comparison of the paradigm of traditional web search engines and generative AI search engines. (a) Web search [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Illustration of our proposed NExT-Search paradigm. NExT-Search introduces a dual feedback mechanism to enhance [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Two mechanisms for leveraging feedback. For example, when users modify sub-queries—such as adding a missing query—the system immediately reprocesses the updated formulation, ensuring that all downstream stages reflect the changes. When users annotate retrieved documents for relevance or apply filtering criteria, the system dynamically re-ranks results, improv￾ing the quality of the knowledge pool before answer synth… view at source ↗

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Works this paper leans on

51 extracted references · 26 canonical work pages

  1. [1]

    Eugene Agichtein, Eric Brill, and Susan Dumais. 2006. Improving web search rank- ing by incorporating user behavior information. InProceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval. 19–26

    2006

  2. [2]

    Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al. 2022. Training a helpful and harmless assistant with reinforcement learning from human feedback.arXiv preprint arXiv:2204.05862(2022)

    arXiv 2022

  3. [3]

    Krisztian Balog and ChengXiang Zhai. 2025. User Simulation in the Era of Gen- erative AI: User Modeling, Synthetic Data Generation, and System Evaluation

    work page 2025

  4. [4]

    Marcia J. Bates. 1989. The design of browsing and berrypicking techniques for the online search interface.Online Review13(5) (1989), 407–424

    work page 1989

  5. [5]

    Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. 2009. Curriculum learning. InProceedings of the 26th annual international conference on machine learning. 41–48

    2009

  6. [6]

    Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender. 2005. Learning to rank using gradient descent. InProceedings of the 22nd international conference on Machine learning. 89–96

    2005

  7. [7]

    2010.Introduction to modern information retrieval

    Gobinda G Chowdhury. 2010.Introduction to modern information retrieval. Facet publishing

    2010

  8. [8]

    Nick Craswell, Onno Zoeter, Michael Taylor, and Bill Ramsey. 2008. An ex- perimental comparison of click position-bias models. InProceedings of the 2008 international conference on web search and data mining. 87–94

    2008

Show all 51 references
  1. [9]

    2010.Search engines: Information retrieval in practice

    W Bruce Croft, Donald Metzler, and Trevor Strohman. 2010.Search engines: Information retrieval in practice. Vol. 520. Addison-Wesley Reading

    2010

  2. [10]

    Sunhao Dai, Chen Xu, Shicheng Xu, Liang Pang, Zhenhua Dong, and Jun Xu

  3. [11]

    Wenqi Fan, Yujuan Ding, Liangbo Ning, Shijie Wang, Hengyun Li, Dawei Yin, Tat-Seng Chua, and Qing Li. 2024. A survey on rag meeting llms: Towards retrieval-augmented large language models. InProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ...

    2024

  4. [12]

    Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang. 2023. Retrieval-augmented generation for large language models: A survey.arXiv preprint arXiv:2312.10997(2023)

    2023 arXiv

  5. [13]

    Jiafeng Guo, Yixing Fan, Qingyao Ai, and W Bruce Croft. 2016. A deep relevance matching model for ad-hoc retrieval. InProceedings of the 25th ACM international on conference on information and knowledge management. 55–64

    2016

  6. [14]

    2017.Search engine society

    Alexander Halavais. 2017.Search engine society. John Wiley & Sons

    2017

  7. [15]

    Ahmed Hassan Awadallah, Ryen W White, Patrick Pantel, Susan T Dumais, and Yi-Min Wang. 2014. Supporting complex search tasks. InProceedings of the 23rd ACM international conference on conference on information and knowledge management. 829–838

    2014

  8. [16]

    2009.Search user interfaces

    Marti Hearst. 2009.Search user interfaces. Cambridge university press

    2009

  9. [17]

    Nadine Höchstötter and Dirk Lewandowski. 2009. What users see–Structures in search engine results pages.Information sciences179, 12 (2009), 1796–1812

    2009

  10. [18]

    Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck. 2013. Learning deep structured semantic models for web search using clickthrough data. InProceedings of the 22nd ACM international conference on Information & Knowledge Management. 2333–2338

    2013

  11. [19]

    Jiepu Jiang, Daqing He, and James Allan. 2014. Searching, browsing, and clicking in a search session: changes in user behavior by task and over time.Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval(2014)

    2014

  12. [20]

    Thorsten Joachims. 2002. Optimizing search engines using clickthrough data. InProceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. 133–142

    2002

  13. [21]

    Thorsten Joachims, Laura Granka, Bing Pan, Helene Hembrooke, and Geri Gay

  14. [22]

    Diane Kelly and Leif Azzopardi. 2015. How many results per page? A study of SERP size, search behavior and user experience. InProceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. 183–192

    2015

  15. [23]

    Diane Kelly and Jaime Teevan. 2003. Implicit feedback for inferring user pref- erence: a bibliography. InAcm Sigir Forum, Vol. 37. ACM New York, NY, USA, 18–28

    2003

  16. [24]

    2011.An introduction to search engines and web navigation

    Mark Levene. 2011.An introduction to search engines and web navigation. John Wiley & Sons

    2011

  17. [25]

    Hang Li, Jun Xu, et al . 2014. Semantic matching in search.Foundations and Trends®in Information Retrieval7, 5 (2014), 343–469

    2014

  18. [26]

    Yongqi Li, Xinyu Lin, Wenjie Wang, Fuli Feng, Liang Pang, Wenjie Li, Liqiang Nie, Xiangnan He, and Tat-Seng Chua. 2024. A survey of generative search and recom- mendation in the era of large language models.arXiv preprint arXiv:2404.16924 (2024)

    2024 arXiv

  19. [27]

    Zhongzhi Li, Duzhen Zhang, Ming-Liang Zhang, Jiaxin Zhang, Zengyan Liu, Yuxuan Yao, Haotian Xu, Junhao Zheng, Pei-Jie Wang, Xiuyi Chen, Yingying Zhang, Fei Yin, Jiahua Dong, Zhijiang Guo, Le Song, and Cheng-Lin Liu. 2025. From System 1 to System 2: A Survey of Reasoning Large ...

    2025

  20. [28]

    Nelson F Liu, Tianyi Zhang, and Percy Liang. 2023. Evaluating verifiability in generative search engines.arXiv preprint arXiv:2304.09848(2023)

    2023 arXiv

  21. [29]

    Tie-Yan Liu et al. 2009. Learning to rank for information retrieval.Foundations and Trends®in Information Retrieval3, 3 (2009), 225–331

    2009

  22. [30]

    Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H Chi. 2018. Modeling task relationships in multi-task learning with multi-gate mixture-of- experts. InProceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 1930–1939

    2018

  23. [31]

    2009.An introduction to information retrieval

    Christopher D Manning. 2009.An introduction to information retrieval

    2009

  24. [32]

    Rafael Mendoza, Isabella Cruz, Richard Liu, Aarav Deshmukh, David Williams, Jesscia Peng, and Rohan Iyer. 2024. Adaptive self-supervised learning strategies for dynamic on-device llm personalization.arXiv preprint arXiv:2409.16973(2024)

    2024 arXiv

  25. [33]

    Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022. Training language models to follow instructions with human feedback.Advances in neural information processing systems35 (...

    2022

  26. [34]

    Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2024. Direct preference optimization: Your language model is secretly a reward model.Advances in Neural Information Processing Systems36 (2024)

    2024

  27. [35]

    Stephen Robertson, Hugo Zaragoza, et al . 2009. The probabilistic relevance framework: BM25 and beyond.Foundations and Trends®in Information Retrieval 3, 4 (2009), 333–389

    2009

  28. [36]

    Alireza Salemi and Hamed Zamani. 2024. Comparing retrieval-augmentation and parameter-efficient fine-tuning for privacy-preserving personalization of large language models.arXiv preprint arXiv:2409.09510(2024)

    2024 arXiv

  29. [37]

    Xuehua Shen, Bin Tan, and ChengXiang Zhai. 2005. Implicit user modeling for personalized search. InProceedings of the 14th ACM international conference on Information and knowledge management. 824–831

    2005

  30. [38]

    Xuehua Shen, Bin Tan, and ChengXiang Zhai. 2007. Privacy protection in per- sonalized search. InACM SIGIR Forum, Vol. 41. ACM New York, NY, USA, 4–17

    2007

  31. [39]

    Rothschild, Daniel G

    Sofia Eleni Spatharioti, David M. Rothschild, Daniel G. Goldstein, and Jake M. Hofman. 2023. Comparing Traditional and LLM-based Search for Consumer Choice: A Randomized Experiment.ArXivabs/2307.03744 (2023)

    2023 arXiv

  32. [40]

    Alysa Ziying Tan, Han Yu, Li zhen Cui, and Qiang Yang. 2021. Towards Person- alized Federated Learning.IEEE Transactions on Neural Networks and Learning Systems34 (2021), 9587–9603

    2021

  33. [41]

    Ryen W White. 2024. Advancing the search frontier with AI agents.Commun. ACM67, 9 (2024), 54–65

    2024

  34. [42]

    Haoyi Xiong, Jiang Bian, Yuchen Li, Xuhong Li, Mengnan Du, Shuaiqiang Wang, Dawei Yin, and Sumi Helal. 2024. When search engine services meet large language models: visions and challenges.IEEE Transactions on Services Computing (2024)

    2024

  35. [43]

    Jun Xu, Xiangnan He, and Hang Li. 2018. Deep learning for matching in search and recommendation. InThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 1365–1368

    2018

  36. [44]

    Yiyan Xu, Jinghao Zhang, Alireza Salemi, Xinting Hu, Wenjie Wang, Fuli Feng, Hamed Zamani, Xiangnan He, and Tat-Seng Chua. 2025. Personalized Generation In Large Model Era: A Survey.arXiv preprint arXiv:2503.02614(2025)

    2025 arXiv

  37. [45]

    Jing Yao, Zhicheng Dou, Jun Xu, and Ji-Rong Wen. 2020. RLPer: A reinforcement learning model for personalized search. InProceedings of The Web Conference

    2020

  38. [46]

    ChengXiang Zhai. 2024. Large Language Models and Future of Information Retrieval: Opportunities and Challenges. InProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. 481–490

    2024

  39. [47]

    Kai Zhang, Yejin Kim, and Xiaozhong Liu. 2024. Personalized llm response generation with parameterized memory injection.arXiv preprint arXiv:2404.03565 (2024)

    2024 arXiv

  40. [48]

    Wayne Xin Zhao, Jing Liu, Ruiyang Ren, and Ji-Rong Wen. 2023. Dense Text Retrieval based on Pretrained Language Models: A Survey.ACM Trans. Inf. Syst. (dec 2023)

    2023

  41. [49]

    Yutao Zhu, Huaying Yuan, Shuting Wang, Jiongnan Liu, Wenhan Liu, Chenlong Deng, Haonan Chen, Zheng Liu, Zhicheng Dou, and Ji-Rong Wen. 2023. Large lan- guage models for information retrieval: A survey.arXiv preprint arXiv:2308.07107 (2023)

    2023

  42. [2017]

    InAcm Sigir Forum, Vol

    Accurately interpreting clickthrough data as implicit feedback. InAcm Sigir Forum, Vol. 51. Acm New York, NY, USA, 4–11

  43. [2024]

    InProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

    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

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Reviewed August 7, 2026 · model on record in the stance chip above.