pith. machine review for the scientific record. sign in

arxiv: 2605.02902 · v1 · submitted 2026-03-30 · 💻 cs.HC · cs.AI

Recognition: 2 theorem links

· Lean Theorem

From Passive Feeds to Guided Discovery: AI-Initiated Interaction for Vague Intent in Content Exploration

Yu Xie , Ying Qi
Authors on Pith no claims yet

Pith reviewed 2026-05-14 22:05 UTC · model grok-4.3

classification 💻 cs.HC cs.AI
keywords vague intentAI-initiated interactionrecommendation feedscontent explorationserendipityproactive interfacesguided discoveryuser effort
0
0 comments X

The pith

Red-Rec's AI-initiated summaries and option selections help users escape repetitive feeds with broader exploration, higher serendipity, and less typing than chat queries.

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

Users often notice that their recommendation feeds have become repetitive yet struggle to name what they want next, a state the paper calls vague intent. Red-Rec responds by analyzing the current feed, summarizing dominant patterns and latent interests, and presenting a small set of clickable exploration options plus at most one follow-up question. In a mixed-design lab study, this proactive approach produced wider content discovery and higher serendipity scores than a user-initiated chat interface while requiring far less typing. Participants mostly selected from the offered options rather than writing their own requests. The system then gradually mixes new items into the feed while aiming to keep users in charge of the direction.

Core claim

The paper establishes that an AI-initiated interface which first summarizes patterns in a user's current recommendation feed, then offers low-effort clickable exploration options and at most one clarifying question, enables effective movement out of repetitive content states. Compared with passive feeds, search, and user-initiated chat, this method produced broader exploration, elevated serendipity ratings, and reduced interaction effort, with users relying primarily on option selection rather than typing.

What carries the argument

Red-Rec, the AI-supported exploration interface that summarizes feed patterns, presents selectable options, limits follow-up input, and gradually blends new content into the existing feed.

If this is right

  • Users achieve broader exploration without having to formulate precise queries.
  • Interaction effort drops because participants select options rather than type.
  • Serendipity ratings rise while users retain a sense of control through limited, visible choices.
  • Recommendation systems can fill the gap between passive browsing and explicit search with proactive but low-demand support.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar option-based guidance could be applied to news or video platforms where repetition is common.
  • The single-question limit might be relaxed in longer sessions to handle varying degrees of vagueness.
  • Live deployments could test whether the approach improves long-term retention compared with standard feeds.
  • The design suggests that proactive AI can reduce cognitive load in information seeking without full automation.

Load-bearing premise

AI-generated summaries of the current feed accurately capture the user's latent interests, and the lab study's mixed-design comparison applies to real-world vague-intent use without creating demand effects.

What would settle it

A field deployment in which users with genuinely repetitive feeds use Red-Rec over multiple sessions and their measured click rates on novel versus familiar items are compared against a passive-feed control.

Figures

Figures reproduced from arXiv: 2605.02902 by Ying Qi, Yu Xie.

Figure 1
Figure 1. Figure 1: The four-stage interaction flow of Red-Rec. subtle animation. Tapping it triggers a feed analysis: the AI com￾putes the category distribution, identifies dominant and underrep￾resented categories, and picks up behavioral signals like dwell time on specific content types. It then opens the conversation panel with a concrete observation: "Your feed is about 75% food and fashion. I noticed you tend to pause o… view at source ↗
read the original abstract

Recommendation feeds work well when people are simply browsing, and search works well when they can formulate a query. Between these two cases is a common but poorly supported state: users feel that their feed has become repetitive, yet cannot clearly specify what they want instead. We refer to this state as vague intent. We present Red-Rec, an AI-supported exploration interface for this middle ground. After a period of browsing, the system summarizes patterns in the current feed (e.g., dominant content categories and possible latent interests), offers clickable exploration options, asks at most one follow-up question, and then gradually blends new content into the feed. The design is motivated by a formative study which found that users often recognize feed staleness but struggle to articulate alternatives, suggesting the need for proactive and low-effort interaction.We evaluated Red-Rec in a mixed-design lab study against three comparison conditions: a passive feed, search, and a user-initiated chat interface. Compared with user-initiated chat, Red-Rec led to broader exploration, higher serendipity ratings, and lower interaction effort. Participants in the AI-initiated condition typed very little , relying mainly on option selection, whereas participants in the user-initiated chat condition typed substantially more . We discuss how proactive, option-based AI support can help users move beyond repetitive feeds without undermining their sense of control, and we outline design implications for recommendation interfaces that support open-ended exploration.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces Red-Rec, an AI-supported exploration interface for 'vague intent' in recommendation feeds—users recognize staleness but struggle to articulate alternatives. After browsing, the system summarizes feed patterns, offers clickable options, asks at most one follow-up question, and blends new content. Motivated by a formative study, it is evaluated in a mixed-design lab study against passive feed, search, and user-initiated chat conditions. The central claims are that Red-Rec produces broader exploration, higher serendipity ratings, and lower interaction effort than user-initiated chat, with participants relying mainly on option selection rather than typing.

Significance. If the comparative results hold after fuller reporting, the work addresses a meaningful gap between passive browsing and explicit search in recommender systems. The emphasis on proactive, low-effort, option-based interaction while preserving user control offers concrete design implications for content platforms. The observation that AI-initiated summaries and selections reduce typing effort is a useful empirical finding for interface research in open-ended exploration scenarios.

major comments (2)
  1. [Abstract / Evaluation] Abstract and Evaluation section: the manuscript reports positive outcomes (broader exploration, higher serendipity, lower effort) from the mixed-design lab study but supplies no participant count, statistical tests, effect sizes, or full protocol details. Without these, the reliability of the central comparative claims cannot be assessed and the results remain unevaluated.
  2. [Evaluation / mixed-design lab study] Study design (mixed-design lab study): the proactive elements (feed summaries + clickable options + at most one question) in a controlled session where vague intent is induced may cue demand characteristics. Participants could select options and rate outcomes favorably to meet perceived experimenter expectations, confounding the reported benefits over user-initiated chat and limiting claims about real-world generalizability.
minor comments (2)
  1. [Abstract] The abstract states that participants in the AI-initiated condition 'typed very little' while the chat condition typed 'substantially more,' but the manuscript would benefit from quantitative measures of typing volume or interaction logs to support this contrast.
  2. [Evaluation] Clarify how 'serendipity ratings' were operationalized and collected (e.g., scale items, timing relative to content exposure) to allow readers to interpret the higher ratings for Red-Rec.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on reporting and study design. We address each major comment below and will revise the manuscript to improve transparency and acknowledge limitations.

read point-by-point responses

  1. Referee: [Abstract / Evaluation] Abstract and Evaluation section: the manuscript reports positive outcomes (broader exploration, higher serendipity, lower effort) from the mixed-design lab study but supplies no participant count, statistical tests, effect sizes, or full protocol details. Without these, the reliability of the central comparative claims cannot be assessed and the results remain unevaluated.

    Authors: We agree that the abstract and Evaluation section lack explicit quantitative details. The current manuscript describes the mixed-design lab study qualitatively but does not report participant numbers, statistical tests, effect sizes, or full protocol. In the revision we will add these elements to the Evaluation section (including N, tests, p-values, and effect sizes) and update the abstract to summarize key metrics. revision: yes

  2. Referee: [Evaluation / mixed-design lab study] Study design (mixed-design lab study): the proactive elements (feed summaries + clickable options + at most one question) in a controlled session where vague intent is induced may cue demand characteristics. Participants could select options and rate outcomes favorably to meet perceived experimenter expectations, confounding the reported benefits over user-initiated chat and limiting claims about real-world generalizability.

    Authors: This concern about demand characteristics is valid for lab studies of novel interfaces. We used a mixed design with counterbalancing and did not reveal hypotheses to participants, but we recognize the proactive AI elements could still influence behavior. We will add a Limitations subsection discussing demand characteristics and generalizability trade-offs while retaining the comparative findings as initial evidence. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical claims rest on study outcomes

full rationale

The paper describes an interface design motivated by a within-paper formative study, followed by a mixed-design lab evaluation against passive feed, search, and user-initiated chat baselines. All load-bearing claims (broader exploration, higher serendipity, lower effort) are presented as direct results of participant behavior and ratings in the controlled study. No equations, fitted parameters, predictions derived from inputs, self-citation chains, or ansatzes appear; the work contains no derivation chain that could reduce to its own definitions. The evaluation is self-contained against its own experimental data.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the domain assumption that users can recognize staleness yet cannot articulate alternatives, drawn from the formative study; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Users recognize when feeds become repetitive but struggle to articulate desired alternatives
    Stated as motivation from the formative study in the abstract.

pith-pipeline@v0.9.0 · 5550 in / 1197 out tokens · 36939 ms · 2026-05-14T22:05:07.296952+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

43 extracted references · 43 canonical work pages · 1 internal anchor

  1. [1]

    James Allen. 1999. Mixed-Initiative Interaction.IEEE Intelligent Systems14, 5 (1999), 14–16

    work page 1999

  2. [2]

    Bennett, Kori Inkpen, Jaime Teevan, Ruth Kiber, and Eric Horvitz

    Saleema Amershi, Dan Weld, Mihaela Vorvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, Shamsi Iqbal, Paul N. Bennett, Kori Inkpen, Jaime Teevan, Ruth Kiber, and Eric Horvitz. 2019. Guidelines for Human-AI Interaction. (2019), 1–13

    work page 2019

  3. [3]

    Eytan Bakshy, Solomon Messing, and Lada A. Adamic. 2015. Exposure to Ide- ologically Diverse News and Opinion on Facebook.Science348, 6239 (2015), 1130–1132

    work page 2015

  4. [4]

    Virginia Braun and Victoria Clarke. 2006. Using Thematic Analysis in Psychology. Qualitative Research in Psychology3, 2 (2006), 77–101

    work page 2006

  5. [5]

    Quick and Dirty

    John Brooke. 1996. SUS: A “Quick and Dirty” Usability Scale.Usability Evaluation in Industry189, 194 (1996), 4–7

    work page 1996

  6. [6]

    Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners.Advances in neural information processing systems33 (2020), 1877–1901

    work page 2020

  7. [7]

    Konstantina Christakopoulou, Filip Radlinski, and Katja Hofmann. 2016. Towards Conversational Recommender Systems. InProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 815– 824

    work page 2016

  8. [8]

    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)

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  9. [9]

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers). 4171–4186

    work page 2019

  10. [10]

    It Makes You Think

    Ian Drosos, Advait Sarkar, Xiaotong (Tone) Xu, and Neil Toronto. 2025. “It Makes You Think”: Provocations Help Restore Critical Thinking to AI-Assisted Knowledge Work. Microsoft Research

    work page 2025

  11. [11]

    Mouzhi Ge, Carla Delgado-Battenfeld, and Dietmar Jannach. 2010. Beyond Accuracy: Evaluating Recommender Systems by Coverage and Serendipity. In Proceedings of the Fourth ACM Conference on Recommender Systems. ACM, 257– 260

    work page 2010

  12. [12]

    Maxwell Harper, Funing Xu, Harmanpreet Kaur, Sara Condliff, Shuo Chang, and Loren Terveen

    F. Maxwell Harper, Funing Xu, Harmanpreet Kaur, Sara Condliff, Shuo Chang, and Loren Terveen. 2015. Putting Users in Control of Their Recommendations. (2015), 3–10

    work page 2015

  13. [13]

    Hernandez-Bocanegra and Jürgen Ziegler

    Diana C. Hernandez-Bocanegra and Jürgen Ziegler. 2023. Explaining Recom- mendations Through Conversations: Dialog Model and the Effects of Interface Type and Interactivity.ACM Transactions on Interactive Intelligent Systems13, 3, 1–47

    work page 2023

  14. [14]

    Eric Horvitz. 1999. Principles of Mixed-Initiative User Interfaces. (1999), 159–166

    work page 1999

  15. [15]

    Dietmar Jannach, Ahtsham Manzoor, Wanling Cai, and Li Chen. 2021. A Survey on Conversational Recommender Systems.Comput. Surveys54, 5, 1–36

    work page 2021

  16. [16]

    Shagun Jhaver et al. 2023. Personalizing Content Moderation on Social Media: User Perspectives on Moderation Choices, Transparency, and Desire for Control. InProceedings of the ACM on Human-Computer Interaction (CSCW), Vol. 7. ACM

    work page 2023

  17. [17]

    Jianchao Ji, Zelong Li, Shuyuan Xu, Wenyue Hua, Yingqiang Ge, Juntao Tan, and Yongfeng Zhang. 2024. Genrec: Large language model for generative recommen- dation. InEuropean Conference on Information Retrieval. Springer, 494–502

    work page 2024

  18. [18]

    Wenjie Jin, Fuli Cai, Honghui Chen, and Xin Zhang. 2022. User-Controllable Recommendation Against Filter Bubbles. (2022), 1251–1261

    work page 2022

  19. [19]

    Knijnenburg, Saadat Bostandjiev, John O’Donovan, and Alfred Kobsa

    Bart P. Knijnenburg, Saadat Bostandjiev, John O’Donovan, and Alfred Kobsa

    work page

  20. [20]

    (2012), 43–50

    Inspectability and Control in Social Recommenders. (2012), 43–50

    work page 2012

  21. [21]

    Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022. Large language models are zero-shot reasoners.Advances in neural information processing systems35 (2022), 22199–22213

    work page 2022

  22. [22]

    Konstan, Qian Zhao, and Jari Veijalainen

    Denis Kotkov, Joseph A. Konstan, Qian Zhao, and Jari Veijalainen. 2019. How Serendipity Improves User Satisfaction with Recommendations: A Large-Scale User Evaluation. InProceedings of the World Wide Web Conference (WWW). ACM, 2854–2860

    work page 2019

  23. [23]

    Matevž Kunaver and Tomaž Požrl. 2017. Diversity in Recommender Systems – A Survey.Knowledge-Based Systems123, 154–162

    work page 2017

  24. [24]

    Gahyun Lee, Mengge Xia, Nels Numan, Xue Qian, David Li, and Yue Chen. 2025. Sensible Agent: A Framework for Unobtrusive Interaction with Proactive AR Agents. InProceedings of the 38th Annual ACM Symposium on User Interface Software and Technology (UIST). ACM

    work page 2025

  25. [25]

    Jinming Li, Wentao Zhang, Tian Wang, Guanglei Xiong, Alan Lu, and Gerard Medioni. 2023. GPT4Rec: A generative framework for personalized recommen- dation and user interests interpretation.arXiv preprint arXiv:2304.03879(2023)

    work page arXiv 2023

  26. [26]

    Xiaopeng Li, Bo Chen, Junda She, Shiteng Cao, You Wang, Qinlin Jia, Haiying He, Zheli Zhou, Zhao Liu, Ji Liu, et al. 2025. A Survey of Generative Recommendation from a Tri-Decoupled Perspective: Tokenization, Architecture, and Optimization. (2025)

    work page 2025

  27. [27]

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

    work page 2024

  28. [28]

    Zhuoran Lu et al. 2024. See Widely, Think Wisely: Toward Designing a Generative Multi-agent System to Burst Filter Bubbles. InProceedings of the CHI Conference on Human Factors in Computing Systems. ACM

    work page 2024

  29. [29]

    Sichun Luo, Yuxuan Yao, Bowei He, Yinya Huang, Aojun Zhou, Xinyi Zhang, Yuanzhang Xiao, Mingjie Zhan, and Linqi Song. 2024. Integrating large language models into recommendation via mutual augmentation and adaptive aggregation. arXiv preprint arXiv:2401.13870(2024)

    work page arXiv 2024

  30. [30]

    Rishabh Mehrotra, Nived Shah, and Benjamin Carterette. 2020. Bandit based Op- timization of Multiple Objectives on a Music Streaming Platform. InProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 3224–3233

    work page 2020

  31. [31]

    Yanming Mei, Yue Wang, Shuai Wang, Qiang Wan, Zhen Li, Chun Yu, and Yuanchun Shi. 2025. InterQuest: A Mixed-Initiative Framework for Dynamic User Interest Modeling in Conversational Search. InProceedings of the 38th Annual ACM Symposium on User Interface Software and Technology (UIST). ACM

    work page 2025

  32. [32]

    Nguyen, Pik-Mai Hui, F

    Tien T. Nguyen, Pik-Mai Hui, F. Maxwell Harper, Loren Terveen, and Joseph A. Konstan. 2014. Exploring the Filter Bubble: The Effect of Using Recommender Systems on Content Diversity.Proceedings of the 23rd International Conference on World Wide Web(2014), 677–686

    work page 2014

  33. [33]

    Eli Pariser. 2011. The Filter Bubble: What the Internet is Hiding from You. Penguin Press

    work page 2011

  34. [34]

    Kelly Garrett, Travis Kriplean, Sean A

    Paul Resnick, R. Kelly Garrett, Travis Kriplean, Sean A. Munson, and Natalie Jo- mini Stroud. 2013. Bursting Your (Filter) Bubble: Strategies for Promoting Diverse Exposure. InProceedings of the 2013 Conference on Computer Supported Coopera- tive Work Companion. ACM, 95–100

    work page 2013

  35. [35]

    Claude E. Shannon. 1948. A Mathematical Theory of Communication.The Bell System Technical Journal27, 3 (1948), 379–423

    work page 1948

  36. [36]

    Yueming Sun and Yi Zhang. 2018. Conversational Recommender System. In Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 235–244

    work page 2018

  37. [37]

    Chun-Hua Tsai and Peter Brusilovsky. 2021. The Effects of Controllability and Explainability in a Social Recommender System.User Modeling and User-Adapted Interaction31 (2021), 591–627

    work page 2021

  38. [38]

    Steven van der Tuin et al. 2025. Chat with the “For You” Algorithm. InProceedings of the 7th ACM Conference on Conversational User Interfaces (CUI). ACM

    work page 2025

  39. [39]

    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 reason- ing in large language models.Advances in neural information processing systems 35 (2022), 24824–24837

    work page 2022

  40. [40]

    Yu Xie, Xing Kai Ren, Ying Qi, and Hu Yao. 2026. SAGE: Sequence-level Adaptive Gradient Evolution for Generative Recommendation.arXiv preprint From Passive Feeds to Guided Discovery: AI-Initiated Interaction for Vague Intent in Content Exploration arXiv:2601.21452(2026)

    work page arXiv 2026

  41. [41]

    Fan Yang, Zheng Chen, Ziyan Jiang, Eunah Cho, Xiaojiang Huang, and Yanbin Lu. 2023. Palr: Personalization aware llms for recommendation.arXiv preprint arXiv:2305.07622(2023)

    work page arXiv 2023

  42. [42]

    Seyun Yoon, Seungjun Oh, Heekyung Park, and Hwajung Hong. 2020. Exploring User Expectations of Proactive AI Systems.Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)4, 4

    work page 2020

  43. [43]

    McNee, Joseph A

    Cai-Nicolas Ziegler, Sean M. McNee, Joseph A. Konstan, and Georg Lausen. 2005. Improving Recommendation Lists Through Topic Diversification. InProceedings of the 14th International Conference on World Wide Web. ACM, 22–32

    work page 2005