Pith. sign in

REVIEW 3 major objections 5 minor 112 references

HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation

T0 review · 3 major / 5 minor · reviewed 2026-08-01 · deepseek-v4-flash

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

Pith's one-line read HyCoRec argues that modeling five preference aspects—items, entities, words, reviews, and knowledge—simultaneously can reduce the rich-get-richer effect in conversational recommendation while improving both accuracy and response diversity.

desk verdict This is a duplicate of an already-published ACL 2024 paper, and the core dynamic Matthew-effect claim is never actually tested; the experiments only show static list-diversity metrics. read the letter →

arxiv 2607.17461 v1 pith:J2ZQ5ZFH submitted 2026-07-20 cs.IR cs.AIcs.HC

Yongsen Zheng , Ruilin Xu , Ziliang Chen , Guohua Wang , Mingjie Qian , Jinghui Qin , Liang Lin This is my paper · ORCID
classification cs.IRcs.AIcs.HC
keywords conversationalrecommendationMattheweffecthypergraphlearningmulti-preferencediversityknowledgegraphslong-tail
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

Conversational recommendation systems chat with users and suggest items in dialogue; they tend to overexpose popular items and neglect niche ones, a pattern known as the Matthew effect. This paper argues that the cure is to learn the user's preferences from many aspects at once—the items themselves, related entities, words in the conversation, item reviews, and knowledge-graph connections. HyCoRec builds three hypergraphs (item, entity, word), encodes reviews with a transformer, encodes knowledge entities with a relational graph network, and fuses the five preference vectors into both the recommender and the dialogue generator. On two benchmarks, it reports higher accuracy and response diversity than previous methods, along with higher catalog coverage and lower isolation of recommendations, which the authors take as evidence the Matthew effect is alleviated.

What carries the argument

The central machinery is the hypergraph: a graph structure whose edges can join more than two nodes at once, letting a single relation capture multi-factor preferences such as genre, brand, and style simultaneously. HyCoRec builds one hypergraph over items from conversation sessions, one over entities in a knowledge graph with k-hop neighbors, and one over words in a lexical knowledge graph; each hypergraph is passed through multi-head hypergraph convolution to produce an aspect preference vector. Reviews are encoded with a transformer and knowledge entities with a relational graph network, and the five preference vectors are pooled into a fused preference that the recommendation head scores

What would settle it

Run HyCoRec and a baseline in a repeated-interaction simulation: at each turn, recommend items, let the user accept a subset, add accepted items to the conversation history, and re-recommend. If HyCoRec's coverage advantage over the baseline shrinks or disappears as turns accumulate, the claim that it alleviates the dynamically amplified Matthew effect is falsified.

Watch

Extended reading notes

Core claim

The central claim is that multi-aspect preference fusion can improve accuracy and diversity together in a conversational recommender. HyCoRec's five preference vectors—item, entity, word, review, and knowledge—are pooled into a single fused preference; the recommendation head scores all candidate items against it, and the decoder attends to it when generating replies. In experiments on REDIAL and TG-REDIAL, the model outperforms all compared baselines on Recall, MRR, and NDCG, and on Dist-2/3/4 response diversity, while showing higher Coverage@k and lower Isolation-Index.

Load-bearing premise

The load-bearing assumption is that static diversity metrics like Coverage@k and Isolation-Index on a fixed test set can stand in for evidence that the Matthew effect is alleviated in a dynamic user-system feedback loop, since the paper never simulates or measures the loop it says amplifies the effect.

Editorial extensions

If this is right

  • If HyCoRec's results hold, a conversational recommender can gain accuracy and diversity from the same fused preference representation, challenging the usual accuracy-diversity trade-off.
  • Higher Coverage@k and lower Isolation-Index mean the model's top-k lists spread over a wider portion of the catalog, directly countering overexposure of popular items.
  • Better Dist-n scores in generated responses imply the dialogue itself can reflect long-tail interests, which may encourage users to mention niche items in later turns.
  • Ablations show each hypergraph aspect is load-bearing: dropping any one of item, entity, or word hypergraphs, or item reviews, lowers recommendation quality.

Reading between the lines

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

  • The dynamic part of the Matthew-effect story is not directly tested. A natural follow-up is a rollout experiment where recommended items are fed back into the conversation for several rounds; if HyCoRec's long-tail advantage persists over rounds, the dynamic claim would be confirmed.
  • The paper itself notes that it does not build a review hypergraph despite modeling review-aspect preference; a review-based hypergraph is an obvious extension that might further widen coverage.
  • Because the evaluation of 'alleviating Matthew effect' relies on static diversity metrics on a fixed test set, the results are best read as evidence of improved long-tail coverage in a snapshot, not of changed long-run consumption patterns; user studies or longitudinal logs would be needed to connect coverage to escape from filter bubbles.
  • The approach suggests a general design: any additional preference source—social relations, timestamps, or multimodal signals—could be plugged in as another hypergraph, making it a template for fairness-oriented conversational recommendation.
Share X Bluesky LinkedIn Reddit HN

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 manuscript proposes HyCoRec, a conversational recommendation framework that learns item-, entity-, word-, review-, and knowledge-aspect preferences through three hypergraph convolutions, a review Transformer, and RGCN encoding, and fuses them for both item prediction and response generation. The stated purpose is to alleviate the Matthew effect, which the authors argue is amplified by dynamic user–system feedback. Experiments on REDIAL and TG-REDIAL compare against a range of baselines and report recommendation metrics (Recall@K, MRR@K, NDCG@K), conversation diversity (Dist-n), coverage/isolation diversity metrics, ablations, hyperparameter plots, and qualitative case studies. The paper claims consistent state-of-the-art results and provides a code link.

Significance. If the empirical claims were reliable, the work would be relevant: it combines multiple preference signals in a hypergraph architecture, targets an important fairness-related problem, and provides code and public-benchmark evaluation. The ablation study shows that each component contributes, and the proposed model does improve over most baselines on most recommendation metrics in Table 1. However, the core contribution—alleviating the dynamic Matthew effect—is not directly evaluated: the diversity metrics are static snapshots, and the paper contains direct numerical contradictions in the conversational and diversity tables. Additionally, the claimed novelty is undercut by an identical prior publication listed in the references. These issues prevent me from recommending acceptance.

major comments (3)
  1. [§4.2–4.3, Tables 1–2] Tables 1 and 2 contain direct counterexamples to the statement that HyCoRec outperforms all baselines. In Table 1, TG-REDIAL N@50 is 0.0245 for HyCoRec versus 0.0256 for MHIM; in Table 2, REDIAL Dist-4 is 0.9523 versus 0.9629 for MHIM. Both tables carry a footnote claiming p<0.05 over all baselines. A lower point estimate cannot be reconciled with a claim of significant improvement over that baseline without further explanation. Please correct the tables, the significance footnote, or the surrounding narrative.
  2. [§4.4, Table 3] The text claims that Coverage@5 on REDIAL improves by 63.75% over MHIM, but the table gives 0.1168 vs 0.1098, which is about 6.38%. More importantly, Coverage@k and Isolation-Index are static top-k list-diversity metrics computed on a fixed test set; they contain no repeated interaction, exposure/feedback loop, or time axis. Sections 1, 3, and 5 state that the Matthew effect is 'increasingly amplified' in dynamic user-system interaction, but no temporal simulation, longitudinal split, or online study is reported anywhere. Thus RQ3 does not test the manuscript's central claim of alleviating the Matthew effect over time.
  3. [References (Zheng et al., 2024e)] The reference list contains 'HyCoRec: Hypergraph-enhanced multi-preference learning for alleviating matthew effect in conversational recommendation' (ACL 2024, pages 2526–2537) with the same title and author list as this submission. The manuscript does not identify this prior publication or state what is new relative to it, and Section 1 still claims this is 'the first work' on multi-aspect preference for alleviating the Matthew effect in CRS. This is a load-bearing novelty problem: the contribution cannot be assessed without knowing the relationship to the prior paper, and it raises a dual-publication concern.
minor comments (5)
  1. [§4.4] 'Cover@5' should be 'Coverage@5' to match the table. The percentage 63.75% is arithmetically inconsistent with the reported values; the correct value is approximately 6.38%.
  2. [§3.2.1, §2.1] Typos: 'hyperege' should be 'hyperedge', 'ConcetNet' should be 'ConceptNet', 'recomemnder' should be 'recommender'.
  3. [§3.2.2, Eq. (16)] Equation (16) writes MHA([P_c;P_h;P_h]), which appears to have a single argument; clarify whether this is a self-attention over the concatenated vector or should be MHA(P_c, P_h, P_h). Similarly, in Eq. (13) the operation Pooling(Ph) after concatenation is not defined.
  4. [§6] The Limitations section says the current version does not include a review-based hypergraph, but the abstract and model description emphasize review-aspect preference. Clarify that review information is encoded by the Transformer rather than by a hypergraph, and adjust the abstract if necessary.
  5. [§4.6, Fig. 2] The hyperparameter analysis is qualitative and Figure 2 lacks axis labels and error bars. Reporting exact values and variance would strengthen the claims.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation found: HyCoRec's predictions are evaluated on public test sets against external baselines; the dynamic Matthew-effect gap and heavy self-citation are correctness/provenance concerns, not tautological reductions.

full rationale

The claimed derivation chain is a standard supervised model: multi-aspect preference vectors (Eqs. 5-12) are fused (Eqs. 13, 16) and trained with cross-entropy (Eqs. 15, 19) to predict held-out items and utterances on REDIAL and TG-REDIAL. No parameter is fitted to the diversity metrics C@k/Iso-Index and then reported as a prediction; Table 3 is an evaluation of the already-trained model against external baselines. The hypergraph convolution is attributed to Bai et al. (2021), the Transformer/attention machinery to Vaswani et al. (2017), and the RGCN/biases to Shang et al. (2023), none of which is an unverified self-citation chain used to prove the central result. Section 4.4's Coverage@k and Isolation-Index are static list metrics, so the paper's stronger temporal claim that the Matthew effect is 'increasingly amplified' via the user-system feedback loop is not directly tested; that is an evidential/validity gap rather than circularity, since the metrics are not inputs to the model and no equation reduces the Matthew-effect claim to them. The reference list contains a prior ACL 2024 HyCoRec paper with the same title/authors and several closely related self-cited hypergraph CRS works, which undermines the 'first work' novelty statement, but the benchmark comparisons would stand independently of those citations, and no load-bearing argument relies on them. Accordingly, no circular step meeting the quoted-reduction standard is present.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The ledger contains no new physical or ontological objects. 'Multi-aspect preferences' and hypergraph structures are computational constructs derived from the data and existing knowledge graphs. The key assumptions are the domain assumptions about the Matthew effect being dynamic and the sufficiency of the five preference aspects, neither of which is empirically tested.

free parameters (6)
  • hop count k for entity/word hypergraph construction = not reported
    Controls how many knowledge-graph neighbors form each hyperedge; chosen by hand and not specified in the text.
  • number of hypergraph convolution layers L = 2 (optimal)
    Determined from hyperparameter analysis (Figure 2); set by validation performance.
  • attention head counts m, j, f, g = not reported
    Multi-head architecture hyperparameters in Eqs. (5)-(9); no values given.
  • balance beta in Eq. (17) = not reported
    Weights the knowledge-aspect and hypergraph-aspect signals in response generation; value unspecified.
  • embedding dimension = not reported
    Only stated that increasing it improves performance; exact value not given.
  • multi-task loss weights for L_r and L_c = not reported
    No weighting scheme for combining recommendation and conversation losses is described.
assumptions (5)
  • standard math Hypergraph convolution operator from Bai et al. (2021) is valid and sufficiently expressive.
    Used as HConv in Eqs. (5)-(8) without proof or adaptation; treated as a given module.
  • domain assumption Users with narrow preferences are more susceptible to the Matthew effect.
    Cited to prior work (Anderson et al. 2020, Hansen et al. 2021) and used to justify multi-preference learning; not independently established here.
  • domain assumption The dynamic user-system feedback loop amplifies the Matthew effect.
    Stated in the introduction and conclusion, but never simulated or empirically tested; the experiments are static.
  • domain assumption DBpedia and ConceptNet alignments adequately cover conversational entities and words.
    Entity- and word-based hypergraphs rely on k-hop neighbors from these KGs; no coverage or failure analysis is provided.
  • ad hoc to paper The five preference aspects (item, entity, word, review, knowledge) are sufficient for alleviating the Matthew effect.
    No theoretical or empirical justification for why exactly these five aspects and no others are selected.

how reviews work

0 comments
Cite this review

Pith. "Pith review of HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation." pith.science (2026). https://pith.science/paper/J2ZQ5ZFH

@misc{pith2026260717461,
  author       = {Pith},
  title        = {Pith review of: HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/J2ZQ5ZFH}},
  note         = {Machine review of arXiv:2607.17461}
}
read the original abstract

The Matthew effect is a notorious issue in Recommender Systems (RSs), \emph{i.e.}, the rich get richer and the poor get poorer, wherein popular items are overexposed while less popular ones are regularly ignored. Most methods examine Matthew effect in static or nearly-static recommendation scenarios. However, the Matthew effect will be increasingly amplified when the user interacts with the system over time. To address these issues, we propose a novel paradigm, Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation (HyCoRec), which aims to alleviate the Matthew effect in conversational recommendation. Concretely, HyCoRec devotes to alleviate the Matthew effect by learning multi-aspect preferences, \emph{i.e.}, item-, entity-, word-, review-, and knowledge-aspect preferences, to effectively generate responses in the conversational task and accurately predict items in the recommendation task when the user chats with the system over time. Extensive experiments conducted on two benchmarks validate that HyCoRec achieves new state-of-the-art performance and the superior of alleviating Matthew effect. Our code is available at https://github.com/zysensmile/HyCoRec.

Figures

Figures reproduced from arXiv: 2607.17461 by the authors.

Figure 1
Figure 1. Overview of our HyCoRec framework, which consists of Hypergraph-Enhanced Multi-Preference Learning [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 3
Figure 3. Case studies to comprehensively understand [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 2
Figure 2. Impact of different hyperparameters. 4.6 Hyperparameters Analysis (RQ5) Next, we investigate the impact of several impor￾tant hyperparameters on the recommendation per￾formance. As depicted in Fig.2, we can observe: Firstly, with the increase of embedding dimen￾sion, the recommendation performance continually improves. This is because the large dimension could encode sufficient high-level feature repre￾sentations. S… view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

112 extracted references · 1 canonical work pages

  1. [1]

    Empirical Methods in Natural Language Processing (Demonstrations) , pages=

    Openke: An open toolkit for knowledge embedding , author=. Empirical Methods in Natural Language Processing (Demonstrations) , pages=

  2. [2]

    Advances in Neural Information Processing Systems , volume=

    Translating embeddings for modeling multi-relational data , author=. Advances in Neural Information Processing Systems , volume=

  3. [3]

    IEEE Transactions on Information Theory , volume=

    On the role of dynamic programming in statistical communication theory , author=. IEEE Transactions on Information Theory , volume=. 1957 , publisher=

    1957

  4. [4]

    Conference on Research and Development in Information Retrieval , pages=

    Interactive recommender system via knowledge graph-enhanced reinforcement learning , author=. Conference on Research and Development in Information Retrieval , pages=

  5. [5]

    Association for the Advancement of Artificial Intelligence , volume=

    Deep reinforcement learning with double q-learning , author=. Association for the Advancement of Artificial Intelligence , volume=

  6. [6]

    The Semantic Web: 15th International Conference , pages=

    Modeling relational data with graph convolutional networks , author=. The Semantic Web: 15th International Conference , pages=. 2018 , organization=

    2018

  7. [7]

    Advances in Neural Information Processing Systems , volume=

    Iterative deep graph learning for graph neural networks: Better and robust node embeddings , author=. Advances in Neural Information Processing Systems , volume=

  8. [8]

    arXiv preprint arXiv:1908.04942 , year=

    Reinforcement learning based graph-to-sequence model for natural question generation , author=. arXiv preprint arXiv:1908.04942 , year=

    arXiv 1908

Show all 112 references
  1. [9]

    arXiv preprint arXiv:1609.02907 , year=

    Semi-supervised classification with graph convolutional networks , author=. arXiv preprint arXiv:1609.02907 , year=

    arXiv

  2. [10]

    The Web Conference , pages=

    Conversational contextual bandit: Algorithm and application , author=. The Web Conference , pages=

  3. [11]

    ACM Transactions on Information Systems , volume=

    Seamlessly unifying attributes and items: Conversational recommendation for cold-start users , author=. ACM Transactions on Information Systems , volume=. 2021 , publisher=

    2021

  4. [12]

    arXiv preprint arXiv:2010.04125 , year=

    Towards topic-guided conversational recommender system , author=. arXiv preprint arXiv:2010.04125 , year=

    2010 arXiv

  5. [13]

    International Conference on Knowledge Discovery and Data Mining , pages=

    Improving conversational recommender systems via knowledge graph based semantic fusion , author=. International Conference on Knowledge Discovery and Data Mining , pages=

  6. [14]

    arXiv preprint arXiv:2005.03954 , year=

    Towards conversational recommendation over multi-type dialogs , author=. arXiv preprint arXiv:2005.03954 , year=

    2005 arXiv

  7. [15]

    Conference on Research and Development in Information Retrieval , pages=

    Towards question-based recommender systems , author=. Conference on Research and Development in Information Retrieval , pages=

  8. [16]

    Conference on Research and Development in Information Retrieval , pages=

    Conversational recommendation: Formulation, methods, and evaluation , author=. Conference on Research and Development in Information Retrieval , pages=

  9. [17]

    Conference on Information and Knowledge Management , pages=

    Towards conversational search and recommendation: System ask, user respond , author=. Conference on Information and Knowledge Management , pages=

  10. [18]

    International Conference on Knowledge Discovery and Data Mining , pages=

    A visual dialog augmented interactive recommender system , author=. International Conference on Knowledge Discovery and Data Mining , pages=

  11. [19]

    International Conference on Multimedia , pages=

    Knowledge-aware multimodal dialogue systems , author=. International Conference on Multimedia , pages=

  12. [20]

    Advances in Neural Information Processing Systems , volume=

    Towards deep conversational recommendations , author=. Advances in Neural Information Processing Systems , volume=

  13. [21]

    International Conference on Knowledge Discovery and Data Mining , pages=

    Towards conversational recommender systems , author=. International Conference on Knowledge Discovery and Data Mining , pages=

  14. [22]

    International Conference on Knowledge Discovery and Data Mining , pages=

    Q&R: A two-stage approach toward interactive recommendation , author=. International Conference on Knowledge Discovery and Data Mining , pages=

  15. [23]

    arXiv preprint arXiv:1908.05391 , year=

    Towards knowledge-based recommender dialog system , author=. arXiv preprint arXiv:1908.05391 , year=

    1908 arXiv

  16. [24]

    The Web Conference , pages=

    Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation , author=. The Web Conference , pages=

  17. [25]

    Conference on Research and Development in Information Retrieval , pages=

    Learning to Infer User Implicit Preference in Conversational Recommendation , author=. Conference on Research and Development in Information Retrieval , pages=

  18. [26]

    Conference on Research and Development in Information Retrieval , pages=

    Conversational Recommendation via Hierarchical Information Modeling , author=. Conference on Research and Development in Information Retrieval , pages=

  19. [27]

    Conference on Research and Development in Information Retrieval , pages=

    Unified conversational recommendation policy learning via graph-based reinforcement learning , author=. Conference on Research and Development in Information Retrieval , pages=

  20. [28]

    Conference on Research and Development in Information Retrieval , pages=

    Learning to ask appropriate questions in conversational recommendation , author=. Conference on Research and Development in Information Retrieval , pages=

  21. [29]

    Web Search and Data Mining , pages=

    Adapting user preference to online feedback in conversational recommendation , author=. Web Search and Data Mining , pages=

  22. [30]

    International Conference on Knowledge Discovery and Data Mining , pages=

    Interactive path reasoning on graph for conversational recommendation , author=. International Conference on Knowledge Discovery and Data Mining , pages=

  23. [31]

    Web Search and Data Mining , pages=

    Estimation-action-reflection: Towards deep interaction between conversational and recommender systems , author=. Web Search and Data Mining , pages=

  24. [32]

    Conversational recommender system , author=

  25. [33]

    2021 , publisher=

    Advances and challenges in conversational recommender systems: A survey , author=. 2021 , publisher=

    2021

  26. [34]

    Hongshen Chen and Xiaorui Liu and Dawei Yin and Jiliang Tang , title =

  27. [35]

    Findings of the Association for Computational Linguistics , pages =

    Yu Lu and Junwei Bao and Yan Song and Zichen Ma and Shuguang Cui and Youzheng Wu and Xiaodong He , title =. Findings of the Association for Computational Linguistics , pages =

  28. [36]

    Web Search and Data Mining , pages =

    Yuanhang Zhou and Kun Zhou and Wayne Xin Zhao and Cheng Wang and Peng Jiang and He Hu , title =. Web Search and Data Mining , pages =

  29. [37]

    International Conference on Knowledge Discovery and Data Mining , pages =

    Konstantina Christakopoulou and Filip Radlinski and Katja Hofmann , title =. International Conference on Knowledge Discovery and Data Mining , pages =

  30. [38]

    Conference on Research

    Yueming Sun and Yi Zhang , title =. Conference on Research

  31. [39]

    Leveraging Historical Interaction Data for Improving Conversational Recommender System , booktitle =

    Kun Zhou and Wayne Xin Zhao and Hui Wang and Sirui Wang and Fuzheng Zhang and Zhongyuan Wang and Ji. Leveraging Historical Interaction Data for Improving Conversational Recommender System , booktitle =

  32. [40]

    The Web Conference , pages =

    Yiming Zhang and Lingfei Wu and Qi Shen and Yitong Pang and Zhihua Wei and Fangli Xu and Bo Long and Jian Pei , title =. The Web Conference , pages =

  33. [41]

    Empirical Methods in Natural Language Processing , pages =

    Shirley Anugrah Hayati and Dongyeop Kang and Qingxiaoyang Zhu and Weiyan Shi and Zhou Yu , title =. Empirical Methods in Natural Language Processing , pages =

  34. [42]

    Advances in Neural Information Processing Systems , pages =

    Raymond Li and Samira Ebrahimi Kahou and Hannes Schulz and Vincent Michalski and Laurent Charlin and Chris Pal , title =. Advances in Neural Information Processing Systems , pages =

  35. [43]

    Towards Topic-Guided Conversational Recommender System , booktitle =

    Kun Zhou and Yuanhang Zhou and Wayne Xin Zhao and Xiaoke Wang and Ji. Towards Topic-Guided Conversational Recommender System , booktitle =

  36. [44]

    Conference on Research and Development in Information Retrieval , pages =

    Zhihui Xie and Tong Yu and Canzhe Zhao and Shuai Li , title =. Conference on Research and Development in Information Retrieval , pages =

  37. [45]

    Association for Computational Linguistics , pages =

    Xiexiong Lin and Weiyu Jian and Jianshan He and Taifeng Wang and Wei Chu , title =. Association for Computational Linguistics , pages =

  38. [46]

    Web Search and Data Mining , pages =

    Xiaoyu Zhang and Xin Xin and Dongdong Li and Wenxuan Liu and Pengjie Ren and Zhumin Chen and Jun Ma and Zhaochun Ren , title =. Web Search and Data Mining , pages =

  39. [47]

    Conference on Research and Development in Information Retrieval , pages =

    Shuokai Li and Ruobing Xie and Yongchun Zhu and Xiang Ao and Fuzhen Zhuang and Qing He , title =. Conference on Research and Development in Information Retrieval , pages =

  40. [48]

    Yingxu Wang and Xiaoru Chen and Jinyuan Fang and Zaiqiao Meng and Shangsong Liang , title =

  41. [49]

    Association for the Advancement of Artificial Intelligence , pages =

    Filip Radlinski and Craig Boutilier and Deepak Ramachandran and Ivan Vendrov , title =. Association for the Advancement of Artificial Intelligence , pages =

  42. [50]

    DBpedia:

    S. DBpedia:. International Semantic Web Conference/Asian Semantic Web Conference , volume =

  43. [51]

    Conference on Research and Development in Information Retrieval , pages =

    Yang Deng and Yaliang Li and Fei Sun and Bolin Ding and Wai Lam , title =. Conference on Research and Development in Information Retrieval , pages =

  44. [52]

    Pointing the Unknown Words , booktitle =

  45. [53]

    Gomez and Lukasz Kaiser and Illia Polosukhin , title =

    Ashish Vaswani and Noam Shazeer and Niki Parmar and Jakob Uszkoreit and Llion Jones and Aidan N. Gomez and Lukasz Kaiser and Illia Polosukhin , title =. Advances in Neural Information Processing Systems , pages =

  46. [54]

    CoRR , volume =

    Tong Zhang and Yong Liu and Peixiang Zhong and Chen Zhang and Hao Wang and Chunyan Miao , title =. CoRR , volume =

  47. [55]

    Conference on Recommender Systems , pages =

    Harald Steck , title =. Conference on Recommender Systems , pages =

  48. [56]

    ACM Transactions on Information Systems , year =

    Chongming Gao and Wenqiang Lei and Jiawei Chen and Shiqi Wang and Xiangnan He and Shijun Li and Biao Li and Yuan Zhang and Peng Jiang , title =. ACM Transactions on Information Systems , year =

  49. [57]

    ACM on Human-Computer Interaction , volume =

    Eslam Hussein and Prerna Juneja and Tanushree Mitra , title =. ACM on Human-Computer Interaction , volume =

  50. [58]

    Shapiro and Mustafa Bilgic , title =

    Ping Liu and Karthik Shivaram and Aron Culotta and Matthew A. Shapiro and Mustafa Bilgic , title =. The Web Conference , pages =

  51. [59]

    Nguyen and Pik

    Tien T. Nguyen and Pik. Exploring the filter bubble: the effect of using recommender systems on content diversity , booktitle =

  52. [60]

    Auditing radicalization pathways on YouTube , booktitle =

    Manoel Horta Ribeiro and Raphael Ottoni and Robert West and Virg. Auditing radicalization pathways on YouTube , booktitle =

  53. [61]

    Conference on User Modeling, Adaptation, and Personalization , pages =

    Larissa Spinelli and Mark Crovella , title =. Conference on User Modeling, Adaptation, and Personalization , pages =

  54. [62]

    An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes , booktitle =

    Mat. An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes , booktitle =

  55. [63]

    Association for the Advancement of Artificial Intelligence , pages =

    Libo Qin and Zhouyang Li and Qiying Yu and Lehan Wang and Wanxiang Che , title =. Association for the Advancement of Artificial Intelligence , pages =

  56. [64]

    Association for the Advancement of Artificial Intelligence , pages =

    Shimin Li and Qinyuan Cheng and Linyang Li and Xipeng Qiu , title =. Association for the Advancement of Artificial Intelligence , pages =

  57. [65]

    Association for the Advancement of Artificial Intelligence , pages =

    Kshitij Mishra and Priyanshu Priya and Asif Ekbal , title =. Association for the Advancement of Artificial Intelligence , pages =

  58. [66]

    Alleviating the Long-Tail Problem in Conversational Recommender Systems , booktitle =

    Zhipeng Zhao and Kun Zhou and Xiaolei Wang and Wayne Xin Zhao and Fan Pan and Zhao Cao and Ji. Alleviating the Long-Tail Problem in Conversational Recommender Systems , booktitle =

  59. [67]

    Epure and Romain Hennequin , title =

    Elena V. Epure and Romain Hennequin , title =. European Chapter of the Association for Computational Linguistics , pages =

  60. [68]

    Conversational Agents for Mental Health and Well-being: Discovering Design Recommendations Using Text Mining , booktitle =

    Abdullah Wahbeh and Mohammad Al. Conversational Agents for Mental Health and Well-being: Discovering Design Recommendations Using Text Mining , booktitle =

  61. [69]

    Conference on Research and Development in Information Retrieval , pages =

    Yuanxing Liu and Weinan Zhang and Baohua Dong and Yan Fan and Hang Wang and Fan Feng and Yifan Chen and Ziyu Zhuang and Hengbin Cui and Yongbin Li and Wanxiang Che , title =. Conference on Research and Development in Information Retrieval , pages =

  62. [70]

    Association for the Advancement of Artificial Intelligence , pages =

    Robyn Speer and Joshua Chin and Catherine Havasi , title =. Association for the Advancement of Artificial Intelligence , pages =

  63. [71]

    Empirical Methods in Natural Language Processing (Demonstrations) , pages =

    Yoon Kim , title =. Empirical Methods in Natural Language Processing (Demonstrations) , pages =

  64. [72]

    User-controllable Recommendation Against Filter Bubbles , booktitle =

    Wenjie Wang and Fuli Feng and Liqiang Nie and Tat. User-controllable Recommendation Against Filter Bubbles , booktitle =

  65. [73]

    The Web Conference , pages =

    Zhenyang Li and Yancheng Dong and Chen Gao and Yizhou Zhao and Dong Li and Jianye Hao and Kai Zhang and Yong Li and Zhi Wang , title =. The Web Conference , pages =

  66. [74]

    Examining the Matthew Effect on YouTube Recommendation System , year=

    Liu, Ying Chieh and Huang, Min Qi , booktitle=. Examining the Matthew Effect on YouTube Recommendation System , year=

  67. [75]

    Conference on Research and Development in Information Retrieval , pages =

    Chongming Gao and Kexin Huang and Jiawei Chen and Yuan Zhang and Biao Li and Peng Jiang and Shiqi Wang and Zhong Zhang and Xiangnan He , title =. Conference on Research and Development in Information Retrieval , pages =

  68. [76]

    The Web Conference , pages =

    Ashton Anderson and Lucas Maystre and Ian Anderson and Rishabh Mehrotra and Mounia Lalmas , title =. The Web Conference , pages =

  69. [77]

    Conference on Web Search and Data Mining , pages =

    Christian Hansen and Rishabh Mehrotra and Casper Hansen and Brian Brost and Lucas Maystre and Mounia Lalmas , title =. Conference on Web Search and Data Mining , pages =

  70. [78]

    Conference on Research and Development in Information Retrieval , pages =

    Yile Liang and Tieyun Qian and Qing Li and Hongzhi Yin , title =. Conference on Research and Development in Information Retrieval , pages =

  71. [79]

    The Web Conference , pages =

    Yu Zheng and Chen Gao and Liang Chen and Depeng Jin and Yong Li , title =. The Web Conference , pages =

  72. [80]

    The Web Conference , pages =

    Yu Zheng and Chen Gao and Xiang Li and Xiangnan He and Yong Li and Depeng Jin , title =. The Web Conference , pages =

  73. [81]

    Conference on Research and Development in Information Retrieval , pages =

    Yingqiang Ge and Shuya Zhao and Honglu Zhou and Changhua Pei and Fei Sun and Wenwu Ou and Yongfeng Zhang , title =. Conference on Research and Development in Information Retrieval , pages =

  74. [82]

    Conference on Research and Development in Information Retrieval , pages =

    Yang Zhang and Fuli Feng and Xiangnan He and Tianxin Wei and Chonggang Song and Guohui Ling and Yongdong Zhang , title =. Conference on Research and Development in Information Retrieval , pages =

  75. [83]

    ACM Transactions on Information Systems , volume=

    A unified multi-task learning framework for multi-goal conversational recommender systems , author=. ACM Transactions on Information Systems , volume=

  76. [84]

    Conference on Research and Development in Information Retrieval , pages=

    User-centric conversational recommendation with multi-aspect user modeling , author=. Conference on Research and Development in Information Retrieval , pages=

  77. [85]

    Conference on Computational Linguistics , pages=

    Suggest me a movie for tonight: Leveraging Knowledge Graphs for Conversational Recommendation , author=. Conference on Computational Linguistics , pages=

  78. [86]

    Conference on Empirical Methods in Natural Language Processing , pages=

    HutCRS: Hierarchical User-Interest Tracking for Conversational Recommender System , author=. Conference on Empirical Methods in Natural Language Processing , pages=

  79. [87]

    Chenzhan Shang and Yupeng Hou and Wayne Xin Zhao and Yaliang Li and Jing Zhang , title =

  80. [88]

    Conference on Information and Knowledge Management , pages=

    A hierarchical recurrent encoder-decoder for generative context-aware query suggestion , author=. Conference on Information and Knowledge Management , pages=

  81. [89]

    Advances in neural information processing systems , volume=

    Attention is all you need , author=. Advances in neural information processing systems , volume=

  82. [90]

    CoRR , year =

    Hao Wang and Zonghu Wang and Weishi Zhang , title =. CoRR , year =

  83. [91]

    Song Bai and Feihu Zhang and Philip H. S. Torr , title =. Pattern Recognit. , volume =

  84. [92]

    Conference on Artificial Intelligence , pages =

    Yifan Feng and Haoxuan You and Zizhao Zhang and Rongrong Ji and Yue Gao , title =. Conference on Artificial Intelligence , pages =

  85. [93]

    Self-Attentive Sequential Recommendation , booktitle =

    Wang. Self-Attentive Sequential Recommendation , booktitle =

  86. [94]

    International Conference on Information and Knowledge Management , pages =

    Fei Sun and Jun Liu and Jian Wu and Changhua Pei and Xiao Lin and Wenwu Ou and Peng Jiang , title =. International Conference on Information and Knowledge Management , pages =

  87. [95]

    the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , pages =

    Jacob Devlin and Ming. the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , pages =

  88. [96]

    Carbonell and Ruslan Salakhutdinov and Quoc V

    Zhilin Yang and Zihang Dai and Yiming Yang and Jaime G. Carbonell and Ruslan Salakhutdinov and Quoc V. Le , title =. Advances in Neural Information Processing Systems , pages =

  89. [97]

    the Association for Computational Linguistics , pages =

    Mike Lewis and Yinhan Liu and Naman Goyal and Marjan Ghazvininejad and Abdelrahman Mohamed and Omer Levy and Veselin Stoyanov and Luke Zettlemoyer , title =. the Association for Computational Linguistics , pages =

  90. [98]

    the Association for Computational Linguistics , pages =

    Yizhe Zhang and Siqi Sun and Michel Galley and Yen. the Association for Computational Linguistics , pages =

  91. [99]

    Tom B. Brown and Benjamin Mann and Nick Ryder and Melanie Subbiah and Jared Kaplan and Prafulla Dhariwal and Arvind Neelakantan and Pranav Shyam and Girish Sastry and Amanda Askell and Sandhini Agarwal and Ariel Herbert. Language Models are Few-Shot Learners , booktitle =

  92. [100]

    IEEE Transactions on Knowledge and Data Engineering , volume=

    Graph-convolved factorization machines for personalized recommendation , author=. IEEE Transactions on Knowledge and Data Engineering , volume=. 2021 , publisher=

    2021

  93. [101]

    Routing User-Interest Markov Tree for Scalable Personalized Knowledge-Aware Recommendation , year=

    Zheng, Yongsen and Wei, Pengxu and Chen, Ziliang and Tang, Chengpei and Lin, Liang , journal=. Routing User-Interest Markov Tree for Scalable Personalized Knowledge-Aware Recommendation , year=

  94. [102]

    CIPL: Counterfactual Interactive Policy Learning to Eliminate Popularity Bias for Online Recommendation , year=

    Zheng, Yongsen and Qin, Jinghui and Wei, Pengxu and Chen, Ziliang and Lin, Liang , journal=. CIPL: Counterfactual Interactive Policy Learning to Eliminate Popularity Bias for Online Recommendation , year=

  95. [103]

    CIREC: Causal Intervention-Inspired Policy Learning to Mitigate Exposure Bias for Interactive Recommendation , year=

    Zheng, Yongsen and Wang, Guohua and Qin, Jinghui and Chen, Ziliang and Lin, Junfan and Wei, Pengxu and Lin, Liang and Lam, Kwok-Yan , journal=. CIREC: Causal Intervention-Inspired Policy Learning to Mitigate Exposure Bias for Interactive Recommendation , year=

  96. [104]

    H ut CRS : Hierarchical User-Interest Tracking for Conversational Recommender System

    Qian, Mingjie and Zheng, Yongsen and Qin, Jinghui and Lin, Liang. H ut CRS : Hierarchical User-Interest Tracking for Conversational Recommender System. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023. doi:10.18653/v1/2023.emnlp-main.635

    2023 doi

  97. [105]

    H y C o R ec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation

    Zheng, Yongsen and Xu, Ruilin and Chen, Ziliang and Wang, Guohua and Qian, Mingjie and Qin, Jinghui and Lin, Liang. H y C o R ec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation. Proceedings of the 62nd Annual Meeti...

    2024 doi

  98. [106]

    2024 , isbn =

    Zheng, Yongsen and Wang, Guohua and Liu, Yang and Lin, Liang , title =. 2024 , isbn =. doi:10.1145/3664647.3680909 , booktitle =

    2024

  99. [107]

    2024 , isbn =

    Zheng, Yongsen and Chen, Ziliang and Qin, Jinghui and Lin, Liang , title =. 2024 , isbn =. doi:10.1609/aaai.v38i8.28794 , booktitle =

    2024 doi

  100. [108]

    Mitigating Matthew Effect: Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning for Conversational Recommendation

    Zheng, Yongsen and Xu, Ruilin and Wang, Guohua and Lin, Liang and Lam, Kwok-Yan. Mitigating Matthew Effect: Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning for Conversational Recommendation. Proceedings of the 2024 Conference on Empirical Methods in Natural La...

    2024 doi

  101. [109]

    Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System

    Zheng, Yongsen and Xie, Zongxuan and Wang, Guohua and Liu, Ziyao and Lin, Liang and Lam, Kwok-Yan. Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System. Findings of the Association for Computational Ling...

    2025 doi

  102. [110]

    H yper CRS : Hypergraph-Aware Multi-Grained Preference Learning to Burst Filter Bubbles in Conversational Recommendation System

    Zheng, Yongsen and Qian, Mingjie and Wang, Guohua and Liu, Yang and Chen, Ziliang and Mao, Mingzhi and Lin, Liang and Lam, Kwok-Yan. H yper CRS : Hypergraph-Aware Multi-Grained Preference Learning to Burst Filter Bubbles in Conversational Recommendation System. Findings of the...

    2025 doi

  103. [111]

    ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=

    Diversity is All You Need: Self-Supervised Hypergraph Learning for Mitigating Popularity Bias in Conversational Recommender System , author=. ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=. 2026 , organization=

    2026

  104. [112]

    Proceedings of the 31st ACM International Conference on Multimedia , pages=

    An order-complexity aesthetic assessment model for aesthetic-aware music recommendation , author=. Proceedings of the 31st ACM International Conference on Multimedia , pages=

Pith tools

Reviewed August 1, 2026 · model on record in the stance chip above.