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REVIEW 2 major objections 6 minor 3 cited by

A Survey on Sequential Recommendation

T0 review · 2 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read This survey claims that sequential recommendation research is best organized by how an item's properties are constructed—unique ID, side information, multi-modal features, or semantic and generative representations.

desk verdict Useful survey of sequential recommendation with a taxonomy claim that only covers half the paper and comparative numbers that do not support the conclusions. read the letter →

arxiv 2412.12770 v2 pith:2DBOQAAW submitted 2024-12-17 cs.IR

Liwei Pan , Weike Pan , Meiyan Wei , Hongzhi Yin , Zhong Ming This is my paper · ORCID
classification cs.IR
keywords sequentialrecommendationsurveytaxonomyitempropertiessideinformationmulti-modalgenerativelargelanguagemodels
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

This paper is a survey that tries to establish a useful roadmap for sequential recommendation by classifying models according to how an item's properties are constructed. It groups the literature into pure ID-based models, models that add side information, multi-modal models, and recent paradigms including generative, LLM-powered, ultra-long-sequence, and data-augmented approaches. A sympathetic reader would care because the taxonomy gives a single axis for comparing efficiency, transferability, and cold-start behavior across a rapidly growing literature, and it identifies open problems such as open-domain, continuous, cloud-edge collaborative, and explainable SR.

What carries the argument

The organizing machinery is the axis of item-property construction. An item in SR can be represented by a unique ID, an ID enriched with categorical, numerical, or graph features, modality embeddings from text, image, or video encoders, or semantic tokens from quantized generative tokenizers; the survey uses this spectrum as a lens through which all models are compared. This axis carries the argument because it converts scattered architectural differences into a single question: what is the item, and what information does that representation carry for transfer, sparsity, and cold-start performance.

What would settle it

Re-run the representative models—SASRec, BERT4Rec, FDSA, S3-Rec, UniSRec, MISSRec, TIGER, SpecGR, P5, and Lite-LLM4Rec—on the same Amazon Office, Game, and Toy splits with identical preprocessing, candidate sets, and hyperparameter budgets; if the ranking implied by Table 5 does not reproduce, the survey's comparative conclusions would be refuted.

Watch

Extended reading notes

Core claim

The survey's central claim is that sequential recommendation (SR) research is most clearly organized by asking how an item's properties are constructed. On that axis it distinguishes models that use only a unique item ID; models that pair the ID with categorical, numerical, knowledge-graph, or social features; models that replace or enrich the ID with multi-modal features such as text, images, and video; and the newest generation that builds semantic IDs, calls on large language models, retrieves from ultra-long sequences, or augments the data itself. The authors argue that this framing exposes the key trade-offs: ID-only models are efficient but cannot transfer across platforms or cope with cold-start, side information and multi-modal features improve sparsity and transferability at higher cost, and generative and LLM-based models point toward open-domain, explainable, and data-centric recommendation.

Load-bearing premise

The load-bearing premise is that the performance numbers copied into Table 5 from three different papers are directly comparable despite likely differences in preprocessing, candidate sampling, and hyperparameters; if those conditions differ, the observed rankings between model families do not stand.

Editorial extensions

If this is right

  • If the taxonomy is right, the next wave of SR designs will be chosen by where they sit on the ID-to-semantic spectrum rather than by backbone architecture alone.
  • Pure ID-based models remain the practical efficiency baseline; side-information and multi-modal variants buy cold-start and transferability improvements at higher compute and tuning cost.
  • Generative SR with semantic IDs can keep embedding tables from growing linearly with item count and shares knowledge between similar items.
  • LLM-powered SR models improve semantic understanding and enable data generation, but their recommendation gains are demonstrated only under the comparisons assembled in the survey.
  • Ultra-long sequences are handled by retrieving a few relevant items before attention, which keeps inference latency bounded.

Reading between the lines

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

  • Inference: If the property-construction axis is adopted as a design principle, it suggests combining modality-based representations for cold-start and transfer with item IDs in warm-start regimes, since the survey notes IDs capture collaborative signals that modality features miss.
  • Inference: The Table 5 comparisons are copied from three separate papers rather than re-run; a controlled re-benchmark under one preprocessing and candidate-sampling protocol would be the natural test of the survey's performance ordering.
  • Inference: The taxonomy could be extended to judge future work by asking whether a new model moves along the axis, for instance toward fully semantic or ID-free representations, rather than by which architecture it uses.
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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

2 major / 6 minor

Summary. The manuscript is a survey of sequential recommendation (SR) that proposes to organize the field from the perspective of how an item's properties are constructed. It defines four formal categories (pure ID-based, ID plus general features, modality-only, and ID plus multimodal features) in Section 3.3, and then devotes separate sections to multi-modal SR, generative SR, LLM-powered SR, ultra-long sequence SR, and data-augmented SR. The survey also collects datasets, evaluation protocols, metrics, and a table of experimental results, and ends with future research directions. Its stated contributions are a comprehensive summary, a four-category taxonomy, a summary of the latest techniques, and empirical studies.

Significance. If the property-construction taxonomy were rigorously applied, the survey would offer a genuinely useful organizing scheme for the heterogeneous SR literature, and its coverage of recent LLM-powered and generative recommendation work is timely. The paper is a secondary source with no derivations or fitted models, so its value lies entirely in the accuracy, completeness, and consistency of its synthesis. The descriptive sections are generally consistent with the cited literature and the reference list is broad, which makes the survey a potentially useful entry point. However, the central novelty claim is only partially realized, and the empirical comparisons in Section 7.4 rest on numbers copied from different papers that are not comparable under a common protocol. These issues do not require new experiments to fix, but they do require reworking the taxonomy and the empirical claims, so the current version needs revision before it can be judged as a reliable roadmap.

major comments (2)
  1. [Section 3.3, Section 3.4, and Figure 5] The claimed four-category taxonomy based on "construction of item properties" is not the organizing principle of the survey. Section 3.3 defines the four categories P(i|S_u), P(i|S_u,N), P(i|F), and P(i|S_u,F), and the abstract and contribution list say the survey studies SR from this new perspective. Yet Section 6.2 introduces generative recommendation with semantic IDs, formalized in Eq. (20) as P(i|S_u)=prod_j p(y_{i,j}|S_u,y_{i,<j}); this is a fifth type of item construction that is absent from the four formal categories. Section 6.3 groups models by whether LLMs are used for direct recommendation, semantic embeddings, or data generation, which is a technique axis; Section 6.4 groups by sequence length; and Section 6.5 groups by data augmentation. Figure 5 itself uses a different top-level taxonomy ('Pure ID-based SR', 'SR with Side Information', 'Recent SR Advancements'). Therefore the paper's central claim of a comprehensive property-construction-based categorization is not supported as stated. Please either restructure the survey so that all covered directions are derived from the item-property axis, or narrow the novelty claim to the categories where the axis is actually used.
  2. [Section 7.4, Table 5] The comparative conclusions in Section 7.4 rest on numbers copied from three different papers ([138], [147], [149]), as the text acknowledges. These results were obtained under potentially different preprocessing, candidate sampling, negative sampling, hyperparameter settings, and data splits; the survey also explicitly says the data in each domain is processed according to the corresponding original papers. The bullet claims (e.g., "LLM-powered SR models show better recommendation performance than traditional SR models" and "SR models with side information achieve better performance than pure ID-based SR ones") treat these non-comparable numbers as though they were produced in a single evaluation. Table 5 also reports no variance or significance measures. As a survey, it is acceptable to quote previously published results, but the authors should either label them strictly as case-by-case reported figures with no cross-paper comparability, or carry out (or cite) a uniform reimplementation. The current wording overstates what the evidence supports.
minor comments (6)
  1. [Section 3.2] The phrase "entity IDs ... are quietly different" should read "quite different".
  2. [Section 7.4, Table 5] In Table 5, the row header "FDSR" appears inconsistent with the text of Section 7.4, which names the model as "FDSA" (reference [121]). Please make the naming consistent.
  3. [Section 6.1] In the sentence "In order words, the representations of item i are obtained...", the phrase should be "In other words".
  4. [Section 7.2] The text says "Compared with random sampling, this split method is more stable," but the method being described is a candidate-sampling procedure, not a data-split method. Please rephrase to avoid the inaccurate use of "split method."
  5. [Figure 5] The text inside Figure 5 appears to contain several OCR-like typographical errors (e.g., "Pur e TD-base d SR", "Neu r al Neiwo r ks"). Please provide a clean version of the figure.
  6. [References] Reference [160] has a typo in its title: "Leveraing" should be "Leveraging".

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a survey whose taxonomic claims are descriptive summaries, not derived predictions.

full rationale

This is a survey paper, so the relevant test is whether any load-bearing claim (e.g., the four-category item-property taxonomy, or the comparative observations in Section 7.4) reduces by construction to its own inputs or to a self-citation chain. It does not. The four categories in Section 3.3 are definitions used to organize later sections, not predictions derived from fitted parameters. Equation (20) formalizes generative recommendation as an autoregressive factorization over semantic-ID tokens; this is a mathematical definition of the generative setup, not a result whose output is equivalent to its input. The survey's citations to the authors' own prior surveys (Refs. [19] and [20]) appear only in the related-work comparison and are not used to justify the central taxonomy. The observational conclusions in Section 7.4 are explicitly copied from external papers for direct comparison; while their comparability is a legitimate methodological concern, no fitting or derivation is performed in this survey, so nothing is statistically forced. The internal inconsistency between the promised four-category property-construction taxonomy and the three-part organization in Figure 5 is a real organizational and novelty limitation, but it is not a circularity: the survey does not derive its conclusions from the taxonomy it fails to apply consistently. No circular step can be quoted from the text, so the honest finding is no significant circularity (score 0).

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

The survey makes no quantitative predictions. The accounting here lists the background assumptions that the survey's conclusions rely on.

assumptions (3)
  • domain assumption The results in Table 5, copied from papers [138, 147, 149], are accurate and were obtained under comparable settings.
    Section 7.4 uses these numbers to rank model families; if the original protocols differ, the conclusions are invalid.
  • ad hoc to paper The proposed four-category taxonomy based on item properties is a valid organizing scheme that captures the key distinctions among SR models.
    Section 3.3 introduces the categorization; no evidence is given that it is exhaustive or that no important papers fall outside the categories.
  • domain assumption Multi-modal features (text, images, videos) share a universal semantic space across domains and platforms.
    Section 3.2 states this to motivate transferability of multimodal SR models; the claim is asserted, not empirically demonstrated in this survey.

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Cite this review

Pith. "Pith review of A Survey on Sequential Recommendation." pith.science (2026). https://pith.science/paper/2DBOQAAW

@misc{pith2026241212770,
  author       = {Pith},
  title        = {Pith review of: A Survey on Sequential Recommendation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2DBOQAAW}},
  note         = {Machine review of arXiv:2412.12770}
}
read the original abstract

Different from most conventional recommendation problems, sequential recommendation focuses on learning users' preferences by exploiting the internal order and dependency among the interacted items, which has received significant attention from both researchers and practitioners. In recent years, we have witnessed great progress and achievements in this field, necessitating a new survey. In this survey, we study the SR problem from a new perspective (i.e., the construction of an item's properties), and summarize the most recent techniques used in sequential recommendation such as pure ID-based SR, SR with side information, multi-modal SR, generative SR, LLM-powered SR, ultra-long SR and data-augmented SR. Moreover, we introduce some frontier research topics in sequential recommendation, e.g., open-domain SR, data-centric SR, could-edge collaborative SR, continuous SR, SR for good, and explainable SR. We believe that our survey could be served as a valuable roadmap for readers in this field.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Beyond Negative Transfer: Disentangled Preference-Guided Diffusion for Cross-Domain Sequential Recommendation

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    A diffusion-based recommender for cross-domain sequential recommendation with disentangled preference guidance claims strong gains over prior baselines, but the reported numbers are internally inconsistent.

  2. Unlocking the Power of Diffusion Models in Sequential Recommendation: A Simple and Effective Approach

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    ADRec applies token-level, per-token diffusion with causal attention to sequential recommendation, reducing embedding collapse and outperforming ten baselines on six datasets.

  3. Beyond Noisy Signals: Dual-Level Denoising for Multi-modal Sequential Recommendation

    cs.IR 2026-07 conditional novelty 4.0 of 10

    A dual-level denoising framework that combines graph Laplacian smoothing and learnable FFT filtering improves multi-modal sequential recommendation on four benchmarks.

Reference graph

Works this paper leans on

199 extracted references · 63 canonical work pages · cited by 3 Pith papers

  1. [138]

    MISSRec: Pre-training and transferring multi-modal interest-aware sequence representation for recommendation

    Wang J, Zeng Z, Wang Y , Wang Y , Lu X, Li T, Yuan J, Zhang R, Zheng H T, Xia S T. MISSRec: Pre-training and transferring multi-modal interest-aware sequence representation for recommendation. In: Proceedings of Liwei Pan et al. 45 the 31st ACM International Conference on Multimedia. 2023, 6548–6557

    work page 2023

  2. [147]

    Inductive generative recommendation via retrieval-based speculation

    Ding Y , Hou Y , Li J, McAuley J. Inductive generative recommendation via retrieval-based speculation. arXiv preprint arXiv:2410.02939, 2024

    arXiv 2024

  3. [149]

    Rethinking large language model architectures for sequential recommendations

    Wang H, Liu X, Fan W, Zhao X, Kini V , Yadav D, Wang F, Wen Z, Tang J, Liu H. Rethinking large language model architectures for sequential recommendations. arXiv preprint arXiv:2402.09543, 2024

    arXiv 2024

  4. [1]

    Self-attentive sequential rec- ommendation

    Kang W C, McAuley J. Self-attentive sequential rec- ommendation. In: Proceedings of the 18th IEEE Inter- national Conference on Data Mining. 2018, 197–206

    2018

  5. [2]

    Translation-based rec- ommendation

    He R, Kang W C, McAuley J. Translation-based rec- ommendation. In: Proceedings of the 11th ACM Con- ference on Recommender Systems. 2017, 161–169 Liwei Pan et al. 39

    2017

  6. [3]

    ICAT-SR: Item categorical attribute integrated sequential recom- mendation

    Yuan X, Duan D, Tong L, Shi L, Zhang C. ICAT-SR: Item categorical attribute integrated sequential recom- mendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Develop- ment in Information Retrieval. 2021, 1687–1691

    2021

  7. [4]

    Non- invasive self-attention for side information fusion in sequential recommendation

    Liu C, Li X, Cai G, Dong Z, Zhu H, Shang L. Non- invasive self-attention for side information fusion in sequential recommendation. In: Proceedings of the AAAI conference on artificial intelligence. 2021, 4249– 4256

    2021

  8. [5]

    BERT: Pre- training of deep bidirectional Transformers for lan- guage understanding

    Kenton J D M W C, Toutanova L K. BERT: Pre- training of deep bidirectional Transformers for lan- guage understanding. In: Proceedings of Proceedings of the 17-th Conference of the North American Chap- ter of the Association for Computational Linguistics: Human Language Technologies. 2019, 4171–4186

    2019

Show all 199 references
  1. [6]

    Deep residual learning for image recognition

    He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recogni- tion. 2016, 770–778

    2016

  2. [7]

    Where to go next for recommender systems? ID- vs

    Yuan Z, Yuan F, Song Y , Li Y , Fu J, Yang F, Pan Y , Ni Y . Where to go next for recommender systems? ID- vs. modality-based recommender models revisited. In: Proceedings of the 46th International ACM SIGIR Con- ference on Research and Development in Information Retrieval. 20...

    2023

  3. [8]

    Towards universal sequence representation learning for recommender systems

    Hou Y , Mu S, Zhao W X, Li Y , Ding B, Wen J R. Towards universal sequence representation learning for recommender systems. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2022, 585–593

    2022

  4. [9]

    GenRec: Large language model for generative recommendation

    Ji J, Li Z, Xu S, Hua W, Ge Y , Tan J, Zhang Y . GenRec: Large language model for generative recommendation. In: Proceedings of the 46th European Conference on Information Retrieval. 2024, 494–502

    2024

  5. [10]

    Improving sequential rec- ommendations with LLMs

    Boz A, Zorgdrager W, Kotti Z, Harte J, Louridas P, Jannach D, Fragkoulis M. Improving sequential rec- ommendations with LLMs. ACM Transactions on Rec- ommender Systems, 2024

    2024

  6. [11]

    Search-based user interest modeling with lifelong sequential behavior data for click-through rate prediction

    Pi Q, Zhou G, Zhang Y , Wang Z, Ren L, Fan Y , Zhu X, Gai K. Search-based user interest modeling with lifelong sequential behavior data for click-through rate prediction. In: Proceedings of the 29th ACM Inter- national Conference on Information and Knowledge Management. 2020, ...

    2020

  7. [12]

    Contrastive learning for sequential recommendation

    Xie X, Sun F, Liu Z, Wu S, Gao J, Zhang J, Ding B, Cui B. Contrastive learning for sequential recommendation. In: Proceedings of the 38th International Conference on Data Engineering. 2022, 1259–1273

    2022

  8. [13]

    A survey on session-based recommender systems

    Wang S, Cao L, Wang Y , Sheng Q Z, Orgun M A, Lian D. A survey on session-based recommender systems. ACM Computing Surveys, 2021, 54(7): 1–38

    2021

  9. [14]

    Graph and sequential neural networks in session-based recommendation: A survey

    Li Z, Yang C, Chen Y , Wang X, Chen H, Xu G, Yao L, Sheng M. Graph and sequential neural networks in session-based recommendation: A survey. ACM Computing Surveys, 2024, 57(2): 1–37

    2024

  10. [15]

    Sequence-aware recommender systems

    Quadrana M, Cremonesi P, Jannach D. Sequence-aware recommender systems. ACM Computing Surveys, 2018, 51(4): 1–36

    2018

  11. [16]

    A survey and taxonomy of se- quential recommender systems for e-commerce prod- uct recommendation

    Nasir M, Ezeife C I. A survey and taxonomy of se- quential recommender systems for e-commerce prod- uct recommendation. SN Computer Science, 2023, 4(6): 708

    2023

  12. [17]

    Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations

    Fang H, Zhang D, Shu Y , Guo G. Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations. ACM Transactions on Infor- mation Systems, 2020, 39(1): 1–42

    2020

  13. [18]

    A survey of sequential recommendation systems: Techniques, evaluation, and future directions

    Boka T F, Niu Z, Neupane R B. A survey of sequential recommendation systems: Techniques, evaluation, and future directions. Information Systems, 2024, 102427

    2024

  14. [19]

    A survey on multi- behavior sequential recommendation

    Chen X, Li Z, Pan W, Ming Z. A survey on multi- behavior sequential recommendation. arXiv preprint arXiv:2308.15701, 2023

    2023

  15. [20]

    A survey on cross-domain sequential recommendation

    Chen S, Xu Z, Pan W, Yang Q, Ming Z. A survey on cross-domain sequential recommendation. In: Pro- ceedings of the 33th International Joint Conference on Artificial Intelligence. 2024, 7989–7998

    2024

  16. [21]

    Contrastive self- supervised learning in recommender systems: A sur- vey

    Jing M, Zhu Y , Zang T, Wang K. Contrastive self- supervised learning in recommender systems: A sur- vey. ACM Transactions on Information Systems, 2023, 42(2): 1–39

    2023

  17. [22]

    A survey on data-centric recommender systems

    Lai R, Chen L, Chen R, Zhang C. A survey on data-centric recommender systems. arXiv preprint arXiv:2401.17878, 2024

    2024 arXiv

  18. [23]

    Side information-driven session-based recommendation: A survey

    Zhang X, Xu B, Li C, Zhou Y , Li L, Lin H. Side information-driven session-based recommendation: A survey. arXiv preprint arXiv:2402.17129, 2024

    2024 arXiv

  19. [24]

    Session- based recommendations with recurrent neural networks

    Hidasi B, Karatzoglou A, Baltrunas L, Tikk D. Session- based recommendations with recurrent neural networks. In: Proceedings of the 4th International Conference on Learning Representations. 2015

    2015

  20. [25]

    Decoupled side information fusion for sequential recommendation

    Xie Y , Zhou P, Kim S. Decoupled side information fusion for sequential recommendation. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2022, 1611–1621

    2022

  21. [26]

    Extracting attentive social temporal excitation for sequential recommendation

    Li Y , Ding Y , Chen B, Xin X, Wang Y , Shi Y , Tang R, Wang D. Extracting attentive social temporal excitation for sequential recommendation. In: Proceedings of the 30th ACM International Conference on Information and Knowledge Management. 2021, 998–1007

    2021

  22. [27]

    Learning vector- 40 Front

    Hou Y , He Z, McAuley J, Zhao W X. Learning vector- 40 Front. Comput. Sci., 2025, 0(0): 1–48 quantized item representation for transferable sequen- tial recommenders. In: Proceedings of the ACM Web Conference 2023. 2023, 1162–1171

    2025

  23. [28]

    Using se- quential and non-sequential patterns in predictive web usage mining tasks

    Mobasher B, Dai H, Luo T, Nakagawa M. Using se- quential and non-sequential patterns in predictive web usage mining tasks. In: Proceedings of the 2th IEEE International Conference on Data Mining. 2002, 669– 672

    2002

  24. [29]

    Fac- torizing personalized Markov chains for next-basket recommendation

    Rendle S, Freudenthaler C, Schmidt-Thieme L. Fac- torizing personalized Markov chains for next-basket recommendation. In: Proceedings of the ACM Web Conference 2010. 2010, 811–820

    2010

  25. [30]

    Recurrent neural networks with top-k gains for session-based recommendations

    Hidasi B, Karatzoglou A. Recurrent neural networks with top-k gains for session-based recommendations. In: Proceedings of the 27th ACM International Con- ference on Information and Knowledge Management. 2018, 843–852

    2018

  26. [31]

    K-plet recurrent neural networks for sequential recommendation

    Lin X, Niu S, Wang Y , Li Y . K-plet recurrent neural networks for sequential recommendation. In: Proceed- ings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval. 2018, 1057–1060

    2018

  27. [32]

    Collaborative sequence prediction for sequential recommender

    Niu S, Zhang R. Collaborative sequence prediction for sequential recommender. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 2017, 2239–2242

    2017

  28. [33]

    Double attention convolutional neural network for sequential recommen- dation

    Chen Q, Li G, Zhou Q, Shi S, Zou D. Double attention convolutional neural network for sequential recommen- dation. ACM Transactions on the Web, 2022, 16(4): 1–23

    2022

  29. [34]

    Personalized top-N sequential recom- mendation via convolutional sequence embedding

    Tang J, Wang K. Personalized top-N sequential recom- mendation via convolutional sequence embedding. In: Proceedings of the 11th ACM International Conference on Web Search and Data Mining. 2018, 565–573

    2018

  30. [35]

    Cos- Rec: 2D convolutional neural networks for sequential recommendation

    Yan A, Cheng S, Kang W C, Wan M, McAuley J. Cos- Rec: 2D convolutional neural networks for sequential recommendation. In: Proceedings of the 28th ACM In- ternational Conference on Information and Knowledge Management. 2019, 2173–2176

    2019

  31. [36]

    BERT4Rec: Sequential recommendation with bidirec- tional encoder representations from Transformer

    Sun F, Liu J, Wu J, Pei C, Lin X, Ou W, Jiang P. BERT4Rec: Sequential recommendation with bidirec- tional encoder representations from Transformer. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2019, 1441–1450

    2019

  32. [37]

    A systematic review and repli- cability study of bert4rec for sequential recommenda- tion

    Petrov A, Macdonald C. A systematic review and repli- cability study of bert4rec for sequential recommenda- tion. In: Proceedings of the 16th ACM Conference on Recommender Systems. 2022, 436–447

    2022

  33. [38]

    Adaptive disen- tangled Transformer for sequential recommendation

    Zhang Y , Wang X, Chen H, Zhu W. Adaptive disen- tangled Transformer for sequential recommendation. In: Proceedings of the 29th ACM SIGKDD Confer- ence on Knowledge Discovery and Data Mining. 2023, 3434–3445

    2023

  34. [39]

    Attention calibration for Transformer-based sequential recommendation

    Zhou P, Ye Q, Xie Y , Gao J, Wang S, Kim J B, You C, Kim S. Attention calibration for Transformer-based sequential recommendation. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 2023, 3595–3605

    2023

  35. [40]

    Sequential recommendation for optimiz- ing both immediate feedback and long-term retention

    Liu Z, Liu S, Zhang Z, Cai Q, Zhao X, Zhao K, Hu L, Jiang P, Gai K. Sequential recommendation for optimiz- ing both immediate feedback and long-term retention. In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Informa- tion Retrieval...

    2024

  36. [41]

    Sequential recommendation via stochastic self-attention

    Fan Z, Liu Z, Wang Y , Wang A, Nazari Z, Zheng L, Peng H, Yu P S. Sequential recommendation via stochastic self-attention. In: Proceedings of the ACM Web Conference 2022. 2022, 2036–2047

    2022

  37. [42]

    Sequential recommendation with self-attentive multi-adversarial network

    Ren R, Liu Z, Li Y , Zhao W X, Wang H, Ding B, Wen J R. Sequential recommendation with self-attentive multi-adversarial network. In: Proceedings of the 43rd International ACM SIGIR conference on Research and Development in Information Retrieval. 2020, 89–98

    2020

  38. [43]

    SSE-PT: Sequen- tial recommendation via personalized Transformer

    Wu L, Li S, Hsieh C J, Sharpnack J. SSE-PT: Sequen- tial recommendation via personalized Transformer. In: Proceedings of the 14th ACM conference on recom- mender systems. 2020, 328–337

    2020

  39. [44]

    STRec: Sparse Transformer for sequential recommendations

    Li C, Wang Y , Liu Q, Zhao X, Wang W, Wang Y , Zou L, Fan W, Li Q. STRec: Sparse Transformer for sequential recommendations. In: Proceedings of the 17th ACM Conference on Recommender Systems. 2023, 101–111

    2023

  40. [45]

    Motif-aware se- quential recommendation

    Cui Z, Cai Y , Wu S, Ma X, Wang L. Motif-aware se- quential recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2021, 1738– 1742

    2021

  41. [46]

    Poincar ´e heterogeneous graph neural networks for sequential recommendation

    Guo N, Liu X, Li S, Ma Q, Gao K, Han B, Zheng L, Guo S, Guo X. Poincar ´e heterogeneous graph neural networks for sequential recommendation. ACM Trans- actions on Information Systems, 2023, 41(3): 1–26

    2023

  42. [47]

    Contrastive learning with bidirectional Transformers for sequential recommendation

    Du H, Shi H, Zhao P, Wang D, Sheng V S, Liu Y , Liu G, Zhao L. Contrastive learning with bidirectional Transformers for sequential recommendation. In: Pro- ceedings of the 31st ACM International Conference on Information and Knowledge Management. 2022, 396–405

    2022

  43. [48]

    De- biased contrastive learning for sequential recommen- dation

    Yang Y , Huang C, Xia L, Huang C, Luo D, Lin K. De- biased contrastive learning for sequential recommen- dation. In: Proceedings of the ACM Web Conference

  44. [49]

    41 Jin D, Li Y

    Lin G, Gao C, Zheng Y , Chang J, Niu Y , Song Y , Li Z, Liwei Pan et al. 41 Jin D, Li Y . Dual-interest factorization-heads attention for sequential recommendation. In: Proceedings of the ACM Web Conference 2023. 2023, 917–927

    2023

  45. [50]

    Dually en- hanced propensity score estimation in sequential rec- ommendation

    Xu C, Xu J, Chen X, Dong Z, Wen J R. Dually en- hanced propensity score estimation in sequential rec- ommendation. In: Proceedings of the 31st ACM In- ternational Conference on Information and Knowledge Management. 2022, 2260–2269

    2022

  46. [51]

    Improving Transformer-based sequential recom- menders through preference editing

    Ma M, Ren P, Chen Z, Ren Z, Liang H, Ma J, De Rijke M. Improving Transformer-based sequential recom- menders through preference editing. ACM Transac- tions on Information Systems, 2023, 41(3): 1–24

    2023

  47. [52]

    Integrating offline reinforcement learning with Transformers for se- quential recommendation

    Xi X, Zhao Y , Liu Q, Ouyang L, Wu Y . Integrating offline reinforcement learning with Transformers for se- quential recommendation. In: Proceedings of the 17th ACM Conference on Recommender Systems. 2023, 1–1

    2023

  48. [53]

    Modeling se- quences as distributions with uncertainty for sequential recommendation

    Fan Z, Liu Z, Wang S, Zheng L, Yu P S. Modeling se- quences as distributions with uncertainty for sequential recommendation. In: Proceedings of the 30th ACM In- ternational Conference on Information and Knowledge Management. 2021, 3019–3023

    2021

  49. [54]

    Transformers4Rec: Bridging the gap be- tween nlp and sequential/session-based recommenda- tion

    Souza Pereira Moreira d G, Rabhi S, Lee J M, Ak R, Oldridge E. Transformers4Rec: Bridging the gap be- tween nlp and sequential/session-based recommenda- tion. In: Proceedings of the 15th ACM Conference on Recommender Systems. 2021, 143–153

    2021

  50. [55]

    Progressive self-attention network with unsymmetrical positional encoding for sequential recommendation

    Zhu Y , Huang B, Jiang S, Yang M, Yang Y , Zhong W. Progressive self-attention network with unsymmetrical positional encoding for sequential recommendation. In: Proceedings of the 45th International ACM SIGIR Con- ference on Research and Development in Information Retrieval. 2...

    2022

  51. [56]

    En- hancing Transformers without self-supervised learning: A loss landscape perspective in sequential recommen- dation

    Lai V , Chen H, Yeh C C M, Xu M, Cai Y , Yang H. En- hancing Transformers without self-supervised learning: A loss landscape perspective in sequential recommen- dation. In: Proceedings of the 17th ACM Conference on Recommender Systems. 2023, 791–797

    2023

  52. [57]

    Diffusion augmentation for sequential recommenda- tion

    Liu Q, Yan F, Zhao X, Du Z, Guo H, Tang R, Tian F. Diffusion augmentation for sequential recommenda- tion. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Manage- ment. 2023, 1576–1586

    2023

  53. [58]

    Di ffuRec: A di ffusion model for sequential recommendation

    Li Z, Sun A, Li C. Di ffuRec: A di ffusion model for sequential recommendation. ACM Transactions on Information Systems, 2023, 42(3): 1–28

    2023

  54. [59]

    Di ff4Rec: Sequential recommendation with curriculum-scheduled diffusion augmentation

    Wu Z, Wang X, Chen H, Li K, Han Y , Sun L, Zhu W. Di ff4Rec: Sequential recommendation with curriculum-scheduled diffusion augmentation. In: Pro- ceedings of the 31st ACM International Conference on Multimedia. 2023, 9329–9335

    2023

  55. [60]

    AutoSe- qRec: Autoencoder for efficient sequential recommen- dation

    Liu S, Liu J, Gu H, Li D, Lu T, Zhang P, Gu N. AutoSe- qRec: Autoencoder for efficient sequential recommen- dation. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Manage- ment. 2023, 1493–1502

    2023

  56. [61]

    Causality-driven user modeling for sequential recommendations over time

    Chen X, Li Q. Causality-driven user modeling for sequential recommendations over time. In: Companion Proceedings of the ACM on Web Conference 2024. 2024, 1400–1406

    2024

  57. [62]

    Leveraging two types of global graph for sequential fashion recom- mendation

    Ding Y , Ma Y , Wong W K, Chua T S. Leveraging two types of global graph for sequential fashion recom- mendation. In: Proceedings of the 2021 International Conference on Multimedia Retrieval. 2021, 73–81

    2021

  58. [63]

    Modeling sequences as star graphs to address over-smoothing in self-attentive sequential recommendation

    Peng B, Chen Z, Parthasarathy S, Ning X. Modeling sequences as star graphs to address over-smoothing in self-attentive sequential recommendation. ACM Transactions on Knowledge Discovery from Data, 2023

    2023

  59. [64]

    Filter-enhanced MLP is all you need for sequential recommendation

    Zhou K, Yu H, Zhao W X, Wen J R. Filter-enhanced MLP is all you need for sequential recommendation. In: Proceedings of the ACM Web Conference 2022. 2022, 2388–2399

    2022

  60. [65]

    TriMLP: A foundational MLP-like archi- tecture for sequential recommendation

    Jiang Y , Xu Y , Yang Y , Yang F, Wang P, Li C, Zhuang F, Xiong H. TriMLP: A foundational MLP-like archi- tecture for sequential recommendation. ACM Transac- tions on Information Systems, 2024, 42(6)

    2024

  61. [66]

    Learning global and multi-granularity local representation with MLP for sequential recommenda- tion

    Long C, Yuan H, Fang J, Xian X, Liu G, Sheng V S, Zhao P. Learning global and multi-granularity local representation with MLP for sequential recommenda- tion. ACM Transactions on Knowledge Discovery from Data, 2024, 18(4): 1–15

    2024

  62. [67]

    Sequential variational autoencoders for collaborative filtering

    Sachdeva N, Manco G, Ritacco E, Pudi V . Sequential variational autoencoders for collaborative filtering. In: Proceedings of the 12th ACM International Conference on Web Search and Data Mining. 2019, 600–608

    2019

  63. [68]

    Hierarchical temporal convolutional networks for dynamic recommender systems

    You J, Wang Y , Pal A, Eksombatchai P, Rosenburg C, Leskovec J. Hierarchical temporal convolutional networks for dynamic recommender systems. In: Pro- ceedings of the ACM Web Conference 2019. 2019, 2236–2246

    2019

  64. [69]

    Gating aug- mented capsule network for sequential recommenda- tion

    Zhang Q, Wu B, Sun Z, Ye Y . Gating aug- mented capsule network for sequential recommenda- tion. Knowledge-Based Systems, 2022, 247: 108817

    2022

  65. [70]

    Time-aware multi-interest capsule network for sequential recommendation

    Wang Z, Shen Y . Time-aware multi-interest capsule network for sequential recommendation. In: Proceed- ings of the 2022 SIAM International Conference on Data Mining. 2022, 558–566

    2022

  66. [71]

    Deep reinforcement learning for list-wise recommendations

    Zhao X, Zhang L, Xia L, Ding Z, Yin D, Tang J. Deep reinforcement learning for list-wise recommendations. arXiv preprint arXiv:1801.00209, 2017

    2017 arXiv

  67. [72]

    Sequence adaptation via re- inforcement learning in recommender systems

    Antaris S, Rafailidis D. Sequence adaptation via re- inforcement learning in recommender systems. In: 42 Front. Comput. Sci., 2025, 0(0): 1–48 Proceedings of the 15th ACM Conference on Recom- mender Systems. 2021, 714–718

    2025

  68. [73]

    AdaMCT: Adaptive mixture of CNN-Transformer for sequential recommendation

    Jiang J, Zhang P, Luo Y , Li C, Kim J B, Zhang K, Wang S, Xie X, Kim S. AdaMCT: Adaptive mixture of CNN-Transformer for sequential recommendation. In: Proceedings of the 32nd ACM International Con- ference on Information and Knowledge Management. 2023, 976–986

    2023

  69. [74]

    Towards accurate and interpretable sequential prediction: A CNN & attention- based feature extractor

    Wang J, Liu Q, Liu Z, Wu S. Towards accurate and interpretable sequential prediction: A CNN & attention- based feature extractor. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2019, 1703–1712

    2019

  70. [75]

    Learning to learn a cold-start sequential recommender

    Huang X, Sang J, Yu J, Xu C. Learning to learn a cold-start sequential recommender. ACM Transactions on Information Systems, 2022, 40(2): 1–25

    2022

  71. [76]

    Global- local graphformer: Towards better understanding of user intentions in sequential recommendation

    Chen H, Huang B, Wang X, Zhou Y , Zhu W. Global- local graphformer: Towards better understanding of user intentions in sequential recommendation. In: Pro- ceedings of the 5th ACM International Conference on Multimedia in Asia. 2023, 1–7

    2023

  72. [77]

    Graph masked autoencoder for sequential recommendation

    Ye Y , Xia L, Huang C. Graph masked autoencoder for sequential recommendation. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2023, 321–330

    2023

  73. [78]

    Learning from sub- stitutable and complementary relations for graph-based sequential product recommendation

    Zhang W, Chen Z, Zha H, Wang J. Learning from sub- stitutable and complementary relations for graph-based sequential product recommendation. ACM Transac- tions on Information Systems, 2021, 40(2): 1–28

    2021

  74. [79]

    APGL4SR: A generic framework with adaptive and personalized global col- laborative information in sequential recommendation

    Yin M, Wang H, Xu X, Wu L, Zhao S, Guo W, Liu Y , Tang R, Lian D, Chen E. APGL4SR: A generic framework with adaptive and personalized global col- laborative information in sequential recommendation. In: Proceedings of the 32nd ACM International Con- ference on Information and ...

    2023

  75. [80]

    End-to-end graph-sequential representation learning for accurate recommendations

    Baikalov V , Frolov E. End-to-end graph-sequential representation learning for accurate recommendations. In: Companion Proceedings of the ACM on Web Con- ference 2024. 2024, 501–504

    2024

  76. [81]

    Recurrent attentive neural networks for sequential recommendation

    Tan L, Xu J, Gong D, Liu F. Recurrent attentive neural networks for sequential recommendation. In: Proceed- ings of the 2023 International Conference on Com- munication Network and Machine Learning. 2023, 356–360

    2023

  77. [82]

    Sheng V S S, Cui Z, Zhou X, Xiong H

    Xu C, Zhao P, Liu Y , Xu J, S. Sheng V S S, Cui Z, Zhou X, Xiong H. Recurrent convolutional neural network for sequential recommendation. In: Proceedings of the ACM Web Conference 2019. 2019, 3398–3404

    2019

  78. [83]

    Learning neighbor user intention on user–item inter- action graphs for better sequential recommendation

    Yu M, Zhu K, Zhao M, Yu J, Xu T, Jin D, Li X, Yu R. Learning neighbor user intention on user–item inter- action graphs for better sequential recommendation. ACM Transactions on the Web, 2024, 18(2): 1–28

    2024

  79. [84]

    Tang J, Belletti F, Jain S, Chen M, Beutel A, Xu C, H. Chi E. Towards neural mixture recommender for long range dependent user sequences. In: Proceedings of the ACM Web Conference 2019. 2019, 1782–1793

    2019

  80. [85]

    Cross-domain self-attentive sequential recommendations

    Alharbi N, Caragea D. Cross-domain self-attentive sequential recommendations. In: Proceedings of the 3rd International Conference on Data Science and Ap- plications. 2022, 601–614

    2022

  81. [86]

    Triple sequence learning for cross-domain recommen- dation

    Ma H, Xie R, Meng L, Chen X, Zhang X, Lin L, Zhou J. Triple sequence learning for cross-domain recommen- dation. ACM Transactions on Information Systems, 2024, 42(4): 1–29

    2024

  82. [87]

    Contrastive cross-domain sequential recommendation

    Cao J, Cong X, Sheng J, Liu T, Wang B. Contrastive cross-domain sequential recommendation. In: Pro- ceedings of the 31st ACM International Conference on Information and Knowledge Management. 2022, 138–147

    2022

  83. [88]

    Time interval aware self- attention for sequential recommendation

    Li J, Wang Y , McAuley J. Time interval aware self- attention for sequential recommendation. In: Proceed- ings of the 13th ACM International Conference on Web Search and Data Mining. 2020, 322–330

    2020

  84. [89]

    Learning heteroge- neous temporal patterns of user preference for timely recommendation

    Cho J, Hyun D, Kang S, Yu H. Learning heteroge- neous temporal patterns of user preference for timely recommendation. In: Proceedings of the ACM Web Conference 2021. 2021, 1274–1283

    2021

  85. [90]

    Multi- behavior sequential Transformer recommender

    Yuan E, Guo W, He Z, Guo H, Liu C, Tang R. Multi- behavior sequential Transformer recommender. In: Proceedings of the 45th International ACM SIGIR Con- ference on Research and Development in Information Retrieval. 2022, 1642–1652

    2022

  86. [91]

    Person- alized behavior-aware Transformer for multi-behavior sequential recommendation

    Su J, Chen C, Lin Z, Li X, Liu W, Zheng X. Person- alized behavior-aware Transformer for multi-behavior sequential recommendation. In: Proceedings of the 31st ACM International Conference on Multimedia. 2023, 6321–6331

    2023

  87. [92]

    Global heterogeneous graph and target interest denois- ing for multi-behavior sequential recommendation

    Li X, Chen H, Yu J, Zhao M, Xu T, Zhang W, Yu M. Global heterogeneous graph and target interest denois- ing for multi-behavior sequential recommendation. In: Proceedings of the 17th ACM International Conference on Web Search and Data Mining. 2024, 387–395

    2024

  88. [93]

    Learning dual dynamic representations on time-sliced user-item interaction graphs for sequential recommendation

    Chen Z, Zhang W, Yan J, Wang G, Wang J. Learning dual dynamic representations on time-sliced user-item interaction graphs for sequential recommendation. In: Proceedings of the 30th ACM International Conference on Information and Knowledge Management. 2021, 231–240

    2021

  89. [94]

    Attention mixtures for time-aware sequential recom- Liwei Pan et al

    Tran V A, Salha-Galvan G, Sguerra B, Hennequin R. Attention mixtures for time-aware sequential recom- Liwei Pan et al. 43 mendation. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Develop- ment in Information Retrieval. 2023, 1821–1826

    2023

  90. [95]

    Continuous-time sequential recommendation with tem- poral graph collaborative Transformer

    Fan Z, Liu Z, Zhang J, Xiong Y , Zheng L, Yu P S. Continuous-time sequential recommendation with tem- poral graph collaborative Transformer. In: Proceedings of the 30th ACM International Conference on Informa- tion and Knowledge Management. 2021, 433–442

    2021

  91. [96]

    Time interval-aware graph with self- attention for sequential recommendation

    Chen Z, Wang W. Time interval-aware graph with self- attention for sequential recommendation. In: Proceed- ings of the 2022 5th International Conference on Al- gorithms, Computing and Artificial Intelligence. 2023, 1–9

    2022

  92. [97]

    Moving forward together: A multi-user pat- tern extraction neural model for sequential recommen- dations

    Du X, Li X. Moving forward together: A multi-user pat- tern extraction neural model for sequential recommen- dations. In: Proceedings of the 6th International Confer- ence on Artificial Intelligence and Pattern Recognition. 2023, 1355–1361

    2023

  93. [98]

    Next- item recommendation with sequential hypergraphs

    Wang J, Ding K, Hong L, Liu H, Caverlee J. Next- item recommendation with sequential hypergraphs. In: Proceedings of the 43rd International ACM SIGIR Con- ference on Research and Development in Information Retrieval. 2020, 1101–1110

    2020

  94. [99]

    Position-enhanced and time-aware graph convolutional network for sequential recommendations

    Huang L, Ma Y , Liu Y , Danny Du B, Wang S, Li D. Position-enhanced and time-aware graph convolutional network for sequential recommendations. ACM Trans- actions on Information Systems, 2023, 41(1): 1–32

    2023

  95. [100]

    Adaptive hier- archical translation-based sequential recommendation

    Zhang Y , He Y , Wang J, Caverlee J. Adaptive hier- archical translation-based sequential recommendation. In: Proceedings of the Web Conference 2020. 2020, 2984–2990

    2020

  96. [101]

    Dual contrastive Transformer for hierarchical preference modeling in se- quential recommendation

    Huang C, Wang S, Wang X, Yao L. Dual contrastive Transformer for hierarchical preference modeling in se- quential recommendation. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2023, 99–109

    2023

  97. [102]

    AutoMLP: Automated MLP for sequential recommendations

    Li M, Zhang Z, Zhao X, Wang W, Zhao M, Wu R, Guo R. AutoMLP: Automated MLP for sequential recommendations. In: Proceedings of the ACM Web Conference 2023. 2023, 1190–1198

    2023

  98. [103]

    A multi-view graph contrastive learning framework for cross-domain sequential rec- ommendation

    Xu Z, Pan W, Ming Z. A multi-view graph contrastive learning framework for cross-domain sequential rec- ommendation. In: Proceedings of the 17th ACM Con- ference on Recommender Systems. 2023, 491–501

    2023

  99. [104]

    DDGHM: Dual dynamic graph with hybrid metric training for cross- domain sequential recommendation

    Zheng X, Su J, Liu W, Chen C. DDGHM: Dual dynamic graph with hybrid metric training for cross- domain sequential recommendation. In: Proceedings of the 30th ACM International Conference on Multimedia. 2022, 471–481

    2022

  100. [105]

    Transformer-based rating- aware sequential recommendation

    Li Y , Li Q, Meng S, Hou J. Transformer-based rating- aware sequential recommendation. In: Proceedings of the 21th International Conference on Algorithms and Architectures for Parallel Processing. 2021, 759–774

    2021

  101. [106]

    Price does matter! modeling price and interest preferences in session-based recommendation

    Zhang X, Xu B, Yang L, Li C, Ma F, Liu H, Lin H. Price does matter! modeling price and interest preferences in session-based recommendation. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2022, 1684–1693

    2022

  102. [107]

    Explain- able interaction-driven user modeling over knowledge graph for sequential recommendation

    Huang X, Fang Q, Qian S, Sang J, Li Y , Xu C. Explain- able interaction-driven user modeling over knowledge graph for sequential recommendation. In: Proceedings of the 27th ACM International Conference on Multi- media. 2019, 548–556

    2019

  103. [108]

    Im- proving sequential recommendation with knowledge- enhanced memory networks

    Huang J, Zhao W X, Dou H, Wen J R, Chang E Y . Im- proving sequential recommendation with knowledge- enhanced memory networks. In: The 41st International ACM SIGIR Conference on Research and Develop- ment in Information Retrieval. 2018, 505–514

    2018

  104. [109]

    Knowledge prompt-tuning for sequential recommendation

    Zhai J, Zheng X, Wang C D, Li H, Tian Y . Knowledge prompt-tuning for sequential recommendation. In: Pro- ceedings of the 31st ACM International Conference on Multimedia. 2023, 6451–6461

    2023

  105. [110]

    KERL: A knowledge-guided reinforcement learning model for sequential recommendation

    Wang P, Fan Y , Xia L, Zhao W X, Niu S, Huang J. KERL: A knowledge-guided reinforcement learning model for sequential recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020, 209–218

    2020

  106. [111]

    User behavior enriched temporal knowledge graphs for sequential recommendation

    Hu H, Guo W, Liu X, Liu Y , Tang R, Zhang R, Kan M Y . User behavior enriched temporal knowledge graphs for sequential recommendation. In: Proceedings of the 17th ACM International Conference on Web Search and Data Mining. 2024, 266–275

    2024

  107. [112]

    Sequential intention-aware recommender based on user interac- tion graph

    Chen J, Cao Y , Zhang F, Sun P, Wei K. Sequential intention-aware recommender based on user interac- tion graph. In: Proceedings of the 2022 International Conference on Multimedia Retrieval. 2022, 118–126

    2022

  108. [113]

    An efficient and effective frame- work for session-based social recommendation

    Chen T, Wong R C W. An efficient and effective frame- work for session-based social recommendation. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 2021, 400–408

    2021

  109. [114]

    A review- driven neural model for sequential recommendation

    Li C, Niu X, Luo X, Chen Z, Quan C. A review- driven neural model for sequential recommendation. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence. 2019, 2866–2872

    2019

  110. [115]

    Improving cur- rent interest with item and review sequential patterns for sequential recommendation

    Zhang J, Mu X, Zhao P, Kang K, Ma C. Improving cur- rent interest with item and review sequential patterns for sequential recommendation. Engineering Applica- tions of Artificial Intelligence, 2021, 104: 104348

    2021

  111. [116]

    Cascaded cross attention for review-based sequential recommen- 44 Front

    Huang B, Luo J, Du W, Pan W, Ming Z. Cascaded cross attention for review-based sequential recommen- 44 Front. Comput. Sci., 2025, 0(0): 1–48 dation. In: Proceedings of the 23th IEEE International Conference on Data Mining. 2023, 170–179

    2025

  112. [117]

    MLP4Rec: A pure MLP architecture for sequential recommendations

    Li M, Zhao X, Lyu C, Zhao M, Wu R, Guo R. MLP4Rec: A pure MLP architecture for sequential recommendations. In: Proceedings of the 31th Inter- national Joint Conference on Artificial Intelligence, IJCAI ’22. 2022

    2022

  113. [118]

    SMLP4Rec: An efficient all-MLP architecture for sequential recommendations

    Gao J, Zhao X, Li M, Zhao M, Wu R, Guo R, Liu Y , Yin D. SMLP4Rec: An efficient all-MLP architecture for sequential recommendations. ACM Transactions on Information Systems, 2024, 42(3): 1–23

    2024

  114. [119]

    RetaGNN: Relational temporal attentive graph neural networks for holistic sequential recom- mendation

    Hsu C, Li C T. RetaGNN: Relational temporal attentive graph neural networks for holistic sequential recom- mendation. In: Proceedings of The Web Conference

  115. [120]

    S3-Rec: Self-supervised learning for sequential recommendation with mutual information maximization

    Zhou K, Wang H, Zhao W X, Zhu Y , Wang S, Zhang F, Wang Z, Wen J R. S3-Rec: Self-supervised learning for sequential recommendation with mutual information maximization. In: Proceedings of the 29th ACM in- ternational Conference on Information and Knowledge Management. 2020, 1893–1902

    2020

  116. [121]

    Feature-level deeper self-attention network for sequential recommendation

    Zhang T, Zhao P, Liu Y , Sheng V S, Xu J, Wang D, Liu G, Zhou X. Feature-level deeper self-attention network for sequential recommendation. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence. 2019, 4320–4326

    2019

  117. [122]

    Adaptive multi- modalities fusion in sequential recommendation sys- tems

    Hu H, Guo W, Liu Y , Kan M Y . Adaptive multi- modalities fusion in sequential recommendation sys- tems. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Manage- ment. 2023, 843–853

    2023

  118. [123]

    Aligned side information fusion method for sequential recommendation

    Wang S, Shen B, Min X, He Y , Zhang X, Zhang L, Zhou J, Mo L. Aligned side information fusion method for sequential recommendation. In: Companion Pro- ceedings of the ACM on Web Conference 2024. 2024, 112–120

    2024

  119. [124]

    Dynamic bi-layer graph learning for context- aware sequential recommendation

    Zhou X, Lumbantoruan R, Ren Y , Chen L, Yang X, Shao J. Dynamic bi-layer graph learning for context- aware sequential recommendation. ACM Transactions on Recommender Systems, 2024, 2(2): 1–23

    2024

  120. [125]

    Contextual sequence modeling for recommendation with recurrent neural networks

    Smirnova E, Vasile F. Contextual sequence modeling for recommendation with recurrent neural networks. In: Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems. 2017, 2–9

    2017

  121. [126]

    Context-aware sequential rec- ommendations withstacked recurrent neural networks

    Rakkappan L, Rajan V . Context-aware sequential rec- ommendations withstacked recurrent neural networks. In: Proceedings of the ACM Web Conference 2019. 2019, 3172–3178

    2019

  122. [127]

    A new sequential pre- diction framework with spatial-temporal embedding

    Zhang J, Lin F, Yang C, Jiang W. A new sequential pre- diction framework with spatial-temporal embedding. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Informa- tion Retrieval. 2022, 2282–2286

    2022

  123. [128]

    Learning dual- view user representations for enhanced sequential rec- ommendation

    Xue L, Yang D, Zhai S, Li Y , Xiao Y . Learning dual- view user representations for enhanced sequential rec- ommendation. ACM Transactions on Information Sys- tems, 2023, 41(4): 1–26

    2023

  124. [129]

    Search-based time-aware graph- enhanced recommendation with sequential behavior data

    Zheng L, Chai H, Chen X, Jin J, Zhang W, Yu Y , Guo X, Ge C, Feng Z. Search-based time-aware graph- enhanced recommendation with sequential behavior data. ACM Transactions on Recommender Systems, 2024, 2(4): 1–29

    2024

  125. [130]

    Sequential and graphical cross-domain recommendations with a multi- view hierarchical transfer gate

    Li H, Yu L, Niu X, Leng Y , Du Q. Sequential and graphical cross-domain recommendations with a multi- view hierarchical transfer gate. ACM Transactions on Knowledge Discovery from Data, 2023, 18(1): 1–28

    2023

  126. [131]

    Online distillation-enhanced multi-modal Transformer for se- quential recommendation

    Ji W, Liu X, Zhang A, Wei Y , Ni Y , Wang X. Online distillation-enhanced multi-modal Transformer for se- quential recommendation. In: Proceedings of the 31st ACM International Conference on Multimedia. 2023, 955–965

    2023

  127. [132]

    Ninerec: A benchmark dataset suite for evaluating transferable recommendation

    Zhang J, Cheng Y , Ni Y , Pan Y , Yuan Z, Fu J, Li Y , Wang J, Yuan F. Ninerec: A benchmark dataset suite for evaluating transferable recommendation. IEEE Trans- actions on Pattern Analysis and Machine Intelligence, 2024

    2024

  128. [133]

    Id-agnostic user behavior pre-training for sequential recommenda- tion

    Mu S, Hou Y , Zhao W X, Li Y , Ding B. Id-agnostic user behavior pre-training for sequential recommenda- tion. In: Proceedings of the 28th China Conference on Information Retrieval. 2022, 16–27

    2022

  129. [134]

    A content-driven micro-video recommendation dataset at scale

    Ni Y , Cheng Y , Liu X, Fu J, Li Y , He X, Zhang Y , Yuan F. A content-driven micro-video recommendation dataset at scale. arXiv preprint arXiv:2309.15379, 2023

    2023 arXiv

  130. [135]

    The elephant in the room: Rethinking the usage of pre-trained language model in sequential recommendation

    Qu Z, Xie R, Xiao C, Sun X, Kang Z. The elephant in the room: Rethinking the usage of pre-trained language model in sequential recommendation. In: Proceedings of the 18th ACM Conference on Recommender Sys- tems, RecSys ’24. 2024, 53–62

    2024

  131. [136]

    Scaling sequential rec- ommendation models with Transformers

    Zivic P, Vazquez H, S´anchez J. Scaling sequential rec- ommendation models with Transformers. In: Proceed- ings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2024, 1567–1577

    2024

  132. [137]

    Text is all you need: Learning language representations for sequential recommendation

    Li J, Wang M, Li J, Fu J, Shen X, Shang J, McAuley J. Text is all you need: Learning language representations for sequential recommendation. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2023, 1258–1267

    2023

  133. [139]

    Mmmlp: Multi-modal multilayer perceptron for sequential recommendations

    Liang J, Zhao X, Li M, Zhang Z, Wang W, Liu H, Liu Z. Mmmlp: Multi-modal multilayer perceptron for sequential recommendations. In: Proceedings of the ACM Web Conference 2023. 2023, 1109–1117

    2023

  134. [140]

    Parallel recurrent neural network architectures for feature-rich session-based recommendations

    Hidasi B, Quadrana M, Karatzoglou A, Tikk D. Parallel recurrent neural network architectures for feature-rich session-based recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems. 2016, 241–248

    2016

  135. [141]

    Rec- ommender systems with generative retrieval

    Rajput S, Mehta N, Singh A, Hulikal Keshavan R, Vu T, Heldt L, Hong L, Tay Y , Tran V , Samost J, others. Rec- ommender systems with generative retrieval. Advances in Neural Information Processing Systems, 2024, 36

    2024

  136. [142]

    Multi-behavior generative recommendation

    Liu Z, Hou Y , McAuley J. Multi-behavior generative recommendation. In: Proceedings of the 33rd ACM In- ternational Conference on Information and Knowledge Management. 2024, 1575–1585

    2024

  137. [143]

    Learnable item tokenization for generative recommendation

    Wang W, Bao H, Lin X, Zhang J, Li Y , Feng F, Ng S K, Chua T S. Learnable item tokenization for generative recommendation. In: Proceedings of the 33rd ACM In- ternational Conference on Information and Knowledge Management. 2024, 2400–2409

    2024

  138. [144]

    EAGER: Two-stream generative recommender with behavior-semantic collaboration

    Wang Y , Xun J, Hong M, Zhu J, Jin T, Lin W, Li H, Li L, Xia Y , Zhao Z, others . EAGER: Two-stream generative recommender with behavior-semantic collaboration. In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2024, 3245– 3254

    2024

  139. [145]

    CoST: Contrastive quantization based semantic tokenization for generative recommendation

    Zhu J, Jin M, Liu Q, Qiu Z, Dong Z, Li X. CoST: Contrastive quantization based semantic tokenization for generative recommendation. In: Proceedings of the 18th ACM Conference on Recommender Systems. 2024, 969–974

    2024

  140. [146]

    MM- GRec: Multimodal generative recommendation with Transformer model

    Liu H, Wei Y , Song X, Guan W, Li Y F, Nie L. MM- GRec: Multimodal generative recommendation with Transformer model. arXiv preprint arXiv:2404.16555, 2024

    2024 arXiv

  141. [148]

    Recommendation as language processing (rlp): A unified pretrain, person- alized prompt & predict paradigm (P5)

    Geng S, Liu S, Fu Z, Ge Y , Zhang Y . Recommendation as language processing (rlp): A unified pretrain, person- alized prompt & predict paradigm (P5). In: Proceed- ings of the 16th ACM Conference on Recommender Systems. 2022, 299–315

    2022

  142. [150]

    Prompt distillation for efficient llm-based recommendation

    Li L, Zhang Y , Chen L. Prompt distillation for efficient llm-based recommendation. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 2023, 1348–1357

    2023

  143. [151]

    Can small language models be good reasoners for sequential recommendation? In: Proceedings of the ACM on Web Conference 2024

    Wang Y , Tian C, Hu B, Yu Y , Liu Z, Zhang Z, Zhou J, Pang L, Wang X. Can small language models be good reasoners for sequential recommendation? In: Proceedings of the ACM on Web Conference 2024. 2024, 3876–3887

    2024

  144. [152]

    Harness- ing large language models for text-rich sequential rec- ommendation

    Zheng Z, Chao W, Qiu Z, Zhu H, Xiong H. Harness- ing large language models for text-rich sequential rec- ommendation. In: Proceedings of the ACM on Web Conference 2024. 2024, 3207–3216

    2024

  145. [153]

    Large language model can interpret latent space of sequential recommender

    Yang Z, Wu J, Luo Y , Zhang J, Yuan Y , Zhang A, Wang X, He X. Large language model can interpret latent space of sequential recommender. arXiv preprint arXiv:2310.20487, 2023

    2023 arXiv

  146. [154]

    TALLRec: An effective and efficient tuning framework to align large language model with recommendation

    Bao K, Zhang J, Zhang Y , Wang W, Feng F, He X. TALLRec: An effective and efficient tuning framework to align large language model with recommendation. In: Proceedings of the 17th ACM Conference on Rec- ommender Systems. 2023, 1007–1014

    2023

  147. [155]

    Distillation matters: Empowering sequen- tial recommenders to match the performance of large language model

    Cui Y , Liu F, Wang P, Wang B, Tang H, Wan Y , Wang J, Chen J. Distillation matters: Empowering sequen- tial recommenders to match the performance of large language model. In: Proceedings of the 18th ACM Conference on Recommender Systems. 2024, 507–517

    2024

  148. [156]

    E4SRec: An elegant effective efficient extensible solution of large language models for sequential recommendation

    Li X, Chen C, Zhao X, Zhang Y , Xing C. E4SRec: An elegant effective efficient extensible solution of large language models for sequential recommendation. arXiv preprint arXiv:2312.02443, 2023

    2023 arXiv

  149. [157]

    Exploring user retrieval integration towards large language models for cross-domain sequential rec- ommendation

    Shen T, Wang H, Zhang J, Zhao S, Li L, Chen Z, Lian D, Chen E. Exploring user retrieval integration towards large language models for cross-domain sequential rec- ommendation. arXiv preprint arXiv:2406.03085, 2024

    2024 arXiv

  150. [158]

    Enhancing sequential rec- ommenders with augmented knowledge from aligned large language models

    Ren Y , Chen Z, Yang X, Li L, Jiang C, Cheng L, Zhang B, Mo L, Zhou J. Enhancing sequential rec- ommenders with augmented knowledge from aligned large language models. In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Ret...

    2024

  151. [159]

    Enhancing sequential recommendation via LLM-based semantic embedding learning

    Hu J, Xia W, Zhang X, Fu C, Wu W, Huan Z, Li A, Tang Z, Zhou J. Enhancing sequential recommendation via LLM-based semantic embedding learning. In: Com- panion Proceedings of the ACM on Web Conference

  152. [160]

    LLM4DSR: Leveraing large lan- guage model for denoising sequential recommendation

    Wang B, Liu F, Chen J, Wu Y , Lou X, Wang J, Feng Y , Chen C, Wang C. LLM4DSR: Leveraing large lan- guage model for denoising sequential recommendation. arXiv preprint arXiv:2408.08208, 2024

    2024 arXiv

  153. [161]

    Xu W, Wu Q, Liang Z, Han J, Ning X, Shi Y , Lin W, 46 Front. Comput. Sci., 2025, 0(0): 1–48 Zhang Y . SLMRec: Distilling large language mod- els into small for sequential recommendation. arXiv preprint arXiv:2405.17890, 2024

    2025 arXiv

  154. [162]

    LLMEmb: Large language model can be a good embedding generator for sequential recommenda- tion

    Liu Q, Wu X, Wang W, Wang Y , Zhu Y , Zhao X, Tian F, Zheng Y . LLMEmb: Large language model can be a good embedding generator for sequential recommenda- tion. arXiv preprint arXiv:2409.19925, 2024

    2024 arXiv

  155. [163]

    A large language model enhanced sequential recommender for joint video and comment recommendation

    Zheng B, Lin Z, Liu E, Yang C, Bai E, Ling C, Zhao W X, Wen J R. A large language model enhanced sequential recommender for joint video and comment recommendation. arXiv preprint arXiv:2403.13574, 2024

    2024 arXiv

  156. [164]

    Large language models enhanced sequential recom- mendation for long-tail user and item

    Liu Q, Wu X, Zhao X, Wang Y , Zhang Z, Tian F, Zheng Y . Large language models enhanced sequential recom- mendation for long-tail user and item. arXiv preprint arXiv:2405.20646, 2024

    2024 arXiv

  157. [165]

    FineRec: Exploring fine-grained sequential recommendation

    Zhang X, Xu B, Wu Y , Zhong Y , Lin H, Ma F. FineRec: Exploring fine-grained sequential recommendation. In: Proceedings of the 47th International ACM SIGIR Con- ference on Research and Development in Information Retrieval. 2024, 1599–1608

    2024

  158. [166]

    Sequential recommendation with latent relations based on large language model

    Yang S, Ma W, Sun P, Ai Q, Liu Y , Cai M, Zhang M. Sequential recommendation with latent relations based on large language model. In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2024, 335–344

    2024

  159. [167]

    Rella: Retrieval-enhanced large language models for lifelong sequential behavior com- prehension in recommendation

    Lin J, Shan R, Zhu C, Du K, Chen B, Quan S, Tang R, Yu Y , Zhang W. Rella: Retrieval-enhanced large language models for lifelong sequential behavior com- prehension in recommendation. In: Proceedings of the ACM on Web Conference 2024. 2024, 3497–3508

    2024

  160. [168]

    Lifelong personalized low-rank adap- tation of large language models for recommendation

    Zhu J, Lin J, Dai X, Chen B, Shan R, Zhu J, Tang R, Yu Y , Zhang W. Lifelong personalized low-rank adap- tation of large language models for recommendation. arXiv preprint arXiv:2408.03533, 2024

    2024 arXiv

  161. [169]

    Breaking the length barrier: LLM- enhanced CTR prediction in long textual user behav- iors

    Geng B, Huan Z, Zhang X, He Y , Zhang L, Yuan F, Zhou J, Mo L. Breaking the length barrier: LLM- enhanced CTR prediction in long textual user behav- iors. In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2024,...

    2024

  162. [170]

    LLMCDSR: En- hancing cross-domain sequential recommendation with large language models

    Xin H, Sun Y , Wang C, Xiong H. LLMCDSR: En- hancing cross-domain sequential recommendation with large language models. ACM Transactions on Informa- tion Systems, 2025

    2025

  163. [171]

    User behavior retrieval for click-through rate prediction

    Qin J, Zhang W, Wu X, Jin J, Fang Y , Yu Y . User behavior retrieval for click-through rate prediction. In: Proceedings of the 43rd International ACM SIGIR Con- ference on Research and Development in Information Retrieval. 2020, 2347–2356

    2020

  164. [172]

    Efficient long sequential user data modeling for click-through rate prediction

    Chen Q, Xu Y , Pei C, Lv S, Zhuang T, Ge J. Efficient long sequential user data modeling for click-through rate prediction. arXiv preprint arXiv:2209.12212, 2022

    2022 arXiv

  165. [173]

    A hierarchical network with user memory matrix for long sequence recommendation

    Dong J, Sun F, Wu T, Wu X, Zhang W, Wang S. A hierarchical network with user memory matrix for long sequence recommendation. Wireless Communications and Mobile Computing, 2022, 2022(1): 5457044

    2022

  166. [174]

    TWIN: Two-stage interest network for lifelong user behavior modeling in CTR prediction at KuaiShou

    Chang J, Zhang C, Fu Z, Zang X, Guan L, Lu J, Hui Y , Leng D, Niu Y , Song Y , others. TWIN: Two-stage interest network for lifelong user behavior modeling in CTR prediction at KuaiShou. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ...

    2023

  167. [175]

    TWIN V2: Scaling ultra- long user behavior sequence modeling for enhanced CTR prediction at KuaiShou

    Si Z, Guan L, Sun Z, Zang X, Lu J, Hui Y , Cao X, Yang Z, Zheng Y , Leng D, others. TWIN V2: Scaling ultra- long user behavior sequence modeling for enhanced CTR prediction at KuaiShou. In: Proceedings of the 33rd ACM International Conference on Information and Knowledge Manag...

    2024

  168. [176]

    R-RNN: Extracting user recent behav- ior sequence for click-through rate prediction

    Gan M, Xiao K. R-RNN: Extracting user recent behav- ior sequence for click-through rate prediction. IEEE Access, 2019, 7: 111767–111777

    2019

  169. [177]

    Sparse attentive memory network for click-through rate prediction with long sequences

    Lin Q, Zhou W J, Wang Y , Da Q, Chen Q G, Wang B. Sparse attentive memory network for click-through rate prediction with long sequences. In: Proceedings of the 31st ACM International Conference on Information and Knowledge Management. 2022, 3312–3321

    2022

  170. [178]

    Practice on long sequential user behavior modeling for click-through rate prediction

    Pi Q, Bian W, Zhou G, Zhu X, Gai K. Practice on long sequential user behavior modeling for click-through rate prediction. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Dis- covery and Data Mining. 2019, 2671–2679

    2019

  171. [179]

    Clustering based behavior sampling with long sequential data for CTR prediction

    Zhang Y , Chen E, Jin B, Wang H, Hou M, Huang W, Yu R. Clustering based behavior sampling with long sequential data for CTR prediction. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2022, 2195–2200

    2022

  172. [180]

    Context-based fast recommendation strategy for long user behavior sequence in meituan waimai

    Feng Z, Xie J, Li K, Qin Y , Wang P, Li Q, Yin B, Li X, Lin W, Wang S. Context-based fast recommendation strategy for long user behavior sequence in meituan waimai. In: Companion Proceedings of the ACM on Web Conference 2024. 2024, 355–363

    2024

  173. [181]

    Cross-domain lifelong sequential modeling for online click-through rate prediction

    Hou R, Yang Z, Ming Y , Lu H, Zheng Z, Chen Y , Zeng Q, Chen M. Cross-domain lifelong sequential modeling for online click-through rate prediction. In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2024, 5116– 5125

    2024

  174. [182]

    Linear recurrent units for sequential recommendation

    Yue Z, Wang Y , He Z, Zeng H, McAuley J, Wang D. Linear recurrent units for sequential recommendation. Liwei Pan et al. 47 In: Proceedings of the 17th ACM International Confer- ence on Web Search and Data Mining. 2024, 930–938

    2024

  175. [183]

    LinRec: Linear attention mechanism for long-term sequential recommender sys- tems

    Liu L, Cai L, Zhang C, Zhao X, Gao J, Wang W, Lv Y , Fan W, Wang Y , He M, others. LinRec: Linear attention mechanism for long-term sequential recommender sys- tems. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retri...

    2023

  176. [184]

    Query-dominant user interest network for large-scale search ranking

    Guo T, Li X, Yang H, Liang X, Yuan Y , Hou J, Ke B, Zhang C, He J, Zhang S, others. Query-dominant user interest network for large-scale search ranking. In: Pro- ceedings of the 32nd ACM International Conference on Information and Knowledge Management. 2023, 629–638

    2023

  177. [185]

    Sampling is all you need on modeling long-term user behaviors for CTR prediction

    Cao Y , Zhou X, Feng J, Huang P, Xiao Y , Chen D, Chen S. Sampling is all you need on modeling long-term user behaviors for CTR prediction. In: Proceedings of the 31st ACM International Conference on Information and Knowledge Management. 2022, 2974–2983

    2022

  178. [186]

    SEMINAR: Search enhanced multi-modal interest network and approximate retrieval for lifelong sequen- tial recommendation

    Shen K, Ding X, Zheng Z, Gong Y , Li Q, Liu Z, Zhang G. SEMINAR: Search enhanced multi-modal interest network and approximate retrieval for lifelong sequen- tial recommendation. arXiv preprint arXiv:2407.10714, 2024

    2024 arXiv

  179. [187]

    Lifelong sequen- tial modeling with personalized memorization for user response prediction

    Ren K, Qin J, Fang Y , Zhang W, Zheng L, Bian W, Zhou G, Xu J, Yu Y , Zhu X, others. Lifelong sequen- tial modeling with personalized memorization for user response prediction. In: Proceedings of the 42nd In- ternational ACM SIGIR Conference on Research and Development in Info...

    2019

  180. [188]

    Uniform sequence better: Time interval aware data augmentation for sequential recommenda- tion

    Dang Y , Yang E, Guo G, Jiang L, Wang X, Xu X, Sun Q, Liu H. Uniform sequence better: Time interval aware data augmentation for sequential recommenda- tion. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2023, 4225–4232

    2023

  181. [189]

    Temporal graph contrastive learning for sequential recommen- dation

    Zhang S, Chen L, Wang C, Li S, Xiong H. Temporal graph contrastive learning for sequential recommen- dation. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2024, 9359–9367

    2024

  182. [190]

    Dataset regeneration for sequential recommendation

    Yin M, Wang H, Guo W, Liu Y , Zhang S, Zhao S, Lian D, Chen E. Dataset regeneration for sequential recommendation. In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2024, 3954–3965

    2024

  183. [191]

    Mutual Wasserstein dis- crepancy minimization for sequential recommendation

    Fan Z, Liu Z, Peng H, Yu P S. Mutual Wasserstein dis- crepancy minimization for sequential recommendation. In: Proceedings of the ACM Web Conference 2023. 2023, 1375–1385

    2023

  184. [192]

    A generic behavior-aware data augmentation framework for sequential recommenda- tion

    Xiao J, Pan W, Ming Z. A generic behavior-aware data augmentation framework for sequential recommenda- tion. In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2024, 1578–1588

    2024

  185. [193]

    Contrastive learning for representation degeneration problem in sequential recommendation

    Qiu R, Huang Z, Yin H, Wang Z. Contrastive learning for representation degeneration problem in sequential recommendation. In: Proceedings of the 15th ACM International Conference on Web Search and Data Min- ing. 2022, 813–823

    2022

  186. [194]

    Efficient noise-decoupling for multi- behavior sequential recommendation

    Han Y , Wang H, Wang K, Wu L, Li Z, Guo W, Liu Y , Lian D, Chen E. Efficient noise-decoupling for multi- behavior sequential recommendation. In: Proceedings of the ACM on Web Conference 2024. 2024, 3297– 3306

    2024

  187. [195]

    Augmenting sequential recommendation with pseudo-prior items via reversely pre-training Transformer

    Liu Z, Fan Z, Wang Y , Yu P S. Augmenting sequential recommendation with pseudo-prior items via reversely pre-training Transformer. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2021, 1608– 1612

    2021

  188. [196]

    Does it look sequential? an analysis of datasets for eval- uation of sequential recommendations

    Klenitskiy A, V olodkevich A, Pembek A, Vasilev A. Does it look sequential? an analysis of datasets for eval- uation of sequential recommendations. In: Proceedings of the 18th ACM Conference on Recommender Sys- tems, RecSys ’24. 2024, 1067–1072

    2024

  189. [197]

    On challenges of evaluating recommender sys- tems in an offline setting

    Sun A. On challenges of evaluating recommender sys- tems in an offline setting. In: Proceedings of the 17th ACM Conference on Recommender Systems. 2023, 1284–1285 Liwei Pan received the BS de- gree from the Hubei University of Technology, China, in 2023. He is currently workin...

    2023

  190. [2003]

    His research interests include software engineering and arti- ficial intelligence

    He is currently a professor with the College of Big Data and Internet, Shenzhen Technology University, China. His research interests include software engineering and arti- ficial intelligence

  191. [2025]

    Hongzhi Yin received the PhD de- gree in Computer Science from Peking University, in 2014

    Her research interests in- clude recommender systems and deep learning. Hongzhi Yin received the PhD de- gree in Computer Science from Peking University, in 2014. He is a full professor and director of the Responsible Big Data Intelligence Lab (RBDI), University of Queens- lan...

    2014

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.