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DiscRec: Disentangled Semantic-Collaborative Modeling for Generative Recommendation

T0 review · 4 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read DiscRec claims that splitting semantic and collaborative signals at the embedding layer, plus item-level position embeddings, consistently improves generative recommendation on four Amazon datasets.

desk verdict A clean, modest plug-in for TIGER/LETTER with an overclaimed disentanglement story: the 'collaborative' branch cannot see cross-item or order information, so the causal interpretation doesn't hold, but the engineering result may. read the letter →

arxiv 2506.15576 v2 pith:NTP7F6UD submitted 2025-06-18 cs.IR

Chang Liu , Yimeng Bai , Xiaoyan Zhao , Yang Zhang , Fuli Feng , Wenge Rong This is my paper · ORCID
classification cs.IR
keywords generativerecommendationdisentangledrepresentationcollaborativesignalsemanticitem-levelpositionembeddinglocalizedattentionsequentialID
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

Generative recommenders predict the next item by autoregressively generating the tokens of its semantic ID, but existing models treat all tokens as interchangeable and mix two very different kinds of signal in one embedding space: semantic signal, which decays hierarchically across token positions, and collaborative signal, which does not. DiscRec claims that this mixture creates conflicting optimization objectives that hurt accuracy, and that both flaws can be fixed at the embedding layer. Its fix is item-level position embeddings, so the model can see which token belongs to which item, plus a dual-branch module that routes semantic and collaborative signals through separate paths and then fuses them with a learned gate. Across four Amazon datasets, the paper reports that DiscRec improves both base models, TIGER and LETTER, on every metric, with the LETTER variant raising Beauty Recall@5 from 0.0424 to 0.0472. If true, the practical payoff is a lightweight architectural change—no extra item table, no second model stream—that makes generative recommenders more accurate without scaling up.

What carries the argument

The load-bearing mechanism is the dual-branch module paired with item-level position embeddings. The position table $V\in\mathbb{R}^{(L+2)\times D}$ makes item structure explicit: the $l$-th token of every item always receives $v_l$, and the special tokens get their own rows, so the model can tell where one item ends and the next begins without an item-specific table. The semantic branch simply passes the original token embeddings through, while the collaborative branch adds $V$ and runs a Transformer with localized attention, whose mask $W[i][j]$ is $0$ when tokens $i$ and $j$ belong to the same item and $-\infty$ otherwise. A softmax gate computed from the inner products of branch outputs with learned gate vectors adaptively fuses the two branches. This design carries the argument because the localized attention mask is the only component that restricts information flow to item granularity, and the gate is what allows the model to keep cross-signal interaction in the deeper Transformer layers.

What would settle it

Run DiscRec-T on Beauty against a variant that keeps the item-level position embeddings but replaces the collaborative branch's localized attention with ordinary full self-attention; if Recall@5 stays near 0.0414 rather than dropping toward the 0.0382 level of the variant without item positions, the localized mask is not what produces the gain. A second check: for a model trained with the collaborative branch outputs shuffled across items, recompute the embedding-norm curves of Figure 6; if the rising collaborative-branch norm pattern persists, the branch is not actually capturing item-level collaboration.

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Extended reading notes

Core claim

The paper's central claim is that the entanglement of semantic and collaborative signals, not model capacity, is what holds generative recommenders back, and that disentangling them at the embedding layer while preserving interaction in deeper layers is enough to release the gain. DiscRec does this with two additions to an existing T5-based recommender. Item-level position embeddings assign each token a position from a table of size $L+2$ (the $L$ positions inside an item plus one each for [EOS] and [BOS]), so tokens in different items share position information by their index within the semantic ID. A dual-branch module then sends the original token embeddings through a semantic branch unchanged, and through a collaborative branch that adds the position embeddings and applies a Transformer whose attention mask $W$ sets to $-\infty$ any attention between tokens of different items. The two branch outputs are combined by a softmax gate, and the resulting representation feeds the encoder and decoder as usual. The paper validates the disentanglement by showing that semantic-branch embeddings reproduce the declining norm pattern of the tokenizer's codebook embeddings across token indices, while collaborative-branch embeddings show the opposite trend, and it reports consistent performance gains: DiscRec-T beats TIGER, and DiscRec-L beats LETTER, on all four datasets and all four metrics.

Load-bearing premise

The load-bearing premise is that the collaborative branch's localized attention, restricted to tokens within the same item, actually captures collaborative signals at item granularity while the semantic branch captures semantic signals; if this separation is not real, the gains attributed to disentanglement could instead come from extra parameters or from item-level position embeddings alone.

Editorial extensions

If this is right

  • If the claim holds, item boundaries should be treated as first-class structure in generative recommenders; shared item-level position embeddings give token sequences item granularity at negligible parameter cost.
  • Disentanglement belongs at the embedding layer, not throughout the model: the paper's AllLayer variant, which applies the dual-branch decoupling to every Transformer layer, falls below the TIGER baseline, whereas embedding-layer-only disentanglement improves it.
  • The localized attention in the collaborative branch is the decisive component: ablating it or replacing it with a single learned query hurts performance more than removing the gating mechanism.
  • Performance gains do not come from added parameters: a five-layer TIGER, which has more parameters than DiscRec, underperforms DiscRec-T, supporting the interpretation that task alignment and signal separation, not capacity, drive the improvements.
  • Because DiscRec is applied on top of two different tokenizers (TIGER's and LETTER's), the benefit transfers across tokenization choices and is not tied to one specific semantic ID construction.

Reading between the lines

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

  • A transferable reading is that any generative recommender—including LLM-based ones—could insert item-level position embeddings and a localized-attention collaborative branch at the input layer; the paper names LLM adaptation as future work but does not test it.
  • The norm-based evidence for disentanglement could be quantified by measuring the correlation between semantic-branch norms and codebook norms across token indices, and by tracking the gate weights to see whether the model shifts from semantic to collaborative signal during decoding.
  • A direct test of the causal claim would compare DiscRec against a variant with the same position embeddings but ordinary full self-attention in the collaborative branch; if performance does not drop, the localized mask is not the source of the gain.
  • The largest gains might be expected in domains where co-occurrence structure and text semantics diverge most, such as news or video; all four evaluation datasets are Amazon product categories, so cross-domain tests would bound the generality.
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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

4 major / 4 minor

Summary. DiscRec proposes a lightweight module for generative sequential recommendation built on TIGER and LETTER. The paper introduces item-level position embeddings (IPE) and a dual-branch input module: a semantic branch passes the original token embeddings through unchanged, and a collaborative branch applies a Transformer with attention restricted to tokens within the same item, followed by a learned gating fusion. The method is evaluated on four Amazon subsets against traditional and generative baselines, with ablations, attention heatmaps, embedding-norm analyses, generalization checks, and several exploratory variants. The code is released.

Significance. The practical idea of injecting item-level structure into token-level generative recommenders is timely, and the paper is clearly written with reproducible training details and publicly available code. If the stated interpretation were correct, DiscRec would be a useful plug-in that separates semantic and collaborative signals at the embedding layer while allowing deeper interaction. However, the architecture as specified does not support the core interpretation: the collaborative branch cannot see cross-item or order-dependent information, so the claimed causal mechanism behind the reported gains is not established. The empirical improvements may be real, but they are not convincingly attributed to signal disentanglement.

major comments (4)
  1. [Section III-A2, Equations (21)-(22)] The paper states that the collaborative branch models 'sequential transition patterns inherent in user interactions,' but this is not what the equations implement. With W[i][j] = -inf whenever tokens i and j belong to different items, the branch output for an item is a function only of that item's own token embeddings and the token-index positions in VX (Equation 15), both of which are invariant to the order of items and to the identities of the other items in the history. The special-token exception couples only [EOS]/[BOS] to the adjacent item. Any cross-item, order-dependent modeling happens later in the original T5 encoder after the fusion gate, so the disentanglement exists only at the input layer. Consequently, the attribution of the Table II gains to disentangled collaborative-signal modeling is unsupported by the architecture.
  2. [Section IV-D, Figure 6 and Figures 4-5] The disentanglement evidence is descriptive rather than demonstrative. The semantic-branch output is identically EX (Equation 19), so its resemblance to code-embedding norms is partially by construction; the collaborative branch consumes the same EX plus IPE, and the increasing norm pattern could be produced by the position embeddings or the gate rather than by collaborative signals. No independent collaborative signal (for example, pretrained collaborative embeddings or item co-occurrence statistics) is used to validate the 'Colla. Emb.' curves. The attention heatmaps in Figures 4-5 show block structure that follows immediately from the four-token item segmentation and the localized mask, so they support item-aware segmentation but not semantic-collaborative disentanglement.
  3. [Section IV-C, Table III] The text claims that 'both w/ IPE and w/ IE lead to consistent performance improvements,' but Table III shows w/ IPE Recall@5 = 0.0372, below TIGER's 0.0381, with mixed results across the four metrics. In addition, the w/o IPE and w/o TF ablations remove entire modules, so their degradations conflate capacity reduction with loss of disentanglement; the w/ 5-Layer control addresses parameter count at the whole-model level but does not isolate the localized-attention branch as the source of the improvement.
  4. [Section IV-B, Table II] No variance estimates, confidence intervals, or significance tests are reported for any metric. Because some reported advantages are small (for example, DiscRec-L vs. LETTER on Arts has a relative Recall@5 gain of only 1.1%), the claim that DiscRec 'consistently outperforms' the baselines is not statistically established on the evidence presented.
minor comments (4)
  1. [Sections I and III-A2] There are several typos: 'imcompatible' should be 'incompatible,' and 'we introduces' should be 'we introduce.' These should be corrected in revision.
  2. [Section IV-C] The text refers to an ablation called 'w/o Attn,' but Table III names the corresponding variant 'w/o TF'; the terminology should be made consistent.
  3. [Figure 7] The legend uses 'DCSM-T' and 'DCSM-L,' while the text and all tables use 'DiscRec-T' and 'DiscRec-L'; these should be aligned.
  4. [Table II] The caption says the best and second-best results are bold and underlined, but in some rows the second-best value is LETTER rather than DiscRec-T (for example, Arts Recall@5). If this is intentional, the caption should state that ranking is per metric and dataset.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: DiscRec's reported gains are measured on held-out test users, and the disentanglement evidence is empirical rather than definitionally enforced.

full rationale

The paper's core derivation is empirical. DiscRec modifies the input embeddings (Eq. 15-16) and adds a dual-branch module (Eq. 19-26) with same-item masked attention (Eq. 21-22), then trains the recommender with the standard negative log-likelihood loss (Eq. 14) on training interactions. Table II reports Recall/NDCG on held-out next items, so no target metric is fitted and then reported as a prediction. TIGER and LETTER are used as backbones and baselines; LETTER involves a co-author, but the comparison is measured, not assumed, and no load-bearing self-citation or imported uniqueness theorem is invoked. The Figure 6 'disentanglement' evidence compares norms of semantic-branch and collaborative-branch outputs after training; although the branch names come from the architecture, the observed norm-decay patterns are not enforced by any loss term, so the evidence is empirical rather than circular. The possible objection that same-item masking cannot encode cross-item sequential transitions is a correctness/interpretation concern about what the collaborative branch actually captures, not a circular derivation of the reported numbers. No circular step meets the required evidence bar.

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

The central claim relies on standard deep learning components (RQ-VAE, T5) and on domain assumptions about what the two branches capture. No free parameters are fitted to the target metrics; the hyperparameters are standard training choices, and no new entities are introduced.

assumptions (3)
  • domain assumption The norm of an embedding vector is a proxy for information capacity (used in the motivation of Section II-C, citing [21] and [22]).
    The paper concludes that code embeddings and token embeddings carry distinct signal distributions based on norm differences. If this proxy does not hold for recommendation embeddings, the motivation for disentanglement weakens, though the model could still work.
  • domain assumption Collaborative signals live at the item level and can be captured by intra-item localized self-attention (Section III-A2, Equations 21 and 22).
    The collaborative branch is built entirely on this assumption. The paper does not validate it with a separate probe task, so the causal story of the method depends on it.
  • domain assumption Semantic IDs produced by RQ-VAE carry hierarchical semantic structure that is distinct from collaborative transition patterns (Section II-A).
    The paper assumes that the tokenization stage yields codebook entries that represent semantic information only, which is the basis for treating the two branches as semantically and collaboratively separated.

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Pith. "Pith review of DiscRec: Disentangled Semantic-Collaborative Modeling for Generative Recommendation." pith.science (2026). https://pith.science/paper/NTP7F6UD

@misc{pith2026250615576,
  author       = {Pith},
  title        = {Pith review of: DiscRec: Disentangled Semantic-Collaborative Modeling for Generative Recommendation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NTP7F6UD}},
  note         = {Machine review of arXiv:2506.15576}
}
read the original abstract

Generative recommendation is emerging as a powerful paradigm that directly generates item predictions, moving beyond traditional matching-based approaches. However, current methods face two key challenges: token-item misalignment, where uniform token-level modeling ignores item-level granularity that is critical for collaborative signal learning, and semantic-collaborative signal entanglement, where collaborative and semantic signals exhibit distinct distributions yet are fused in a unified embedding space, leading to conflicting optimization objectives that limit the recommendation performance. To address these issues, we propose DiscRec, a novel framework that enables Disentangled Semantic-Collaborative signal modeling with flexible fusion for generative Recommendation. First, DiscRec introduces item-level position embeddings, assigned based on indices within each semantic ID, enabling explicit modeling of item structure in input token sequences. Second, DiscRec employs a dual-branch module to disentangle the two signals at the embedding layer: a semantic branch encodes semantic signals using original token embeddings, while a collaborative branch applies localized attention restricted to tokens within the same item to effectively capture collaborative signals. A gating mechanism subsequently fuses both branches while preserving the model's ability to model sequential dependencies. Extensive experiments on four real-world datasets demonstrate that DiscRec effectively decouples these signals and consistently outperforms state-of-the-art baselines. Our codes are available on https://github.com/Ten-Mao/DiscRec.

Figures

Figures reproduced from arXiv: 2506.15576 by the authors.

Figure 1
Figure 1. Comparison of the distribution between code embed [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of the generative recommendation paradigm, [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Overview of the proposed DiscRec framework. The upper part illustrates the generative recommendation pipeline, which [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Layer-wise attention heatmaps for the four Transformer [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 6
Figure 6. Figure 6: Embedding norm distribution comparison of code [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: Performance comparison on different sequence lengths [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]
Figure 5
Figure 5. Figure 5: Layer-wise attention heatmaps for the four Transformer [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]

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Cited by 2 Pith papers

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  2. SPARC: Sequence-aware Progressive Attribute Routing and Compression Framework for Generative Recommendation

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Reference graph

Works this paper leans on

45 extracted references · 32 canonical work pages · cited by 2 Pith papers

  1. [1]

    Generative rec- ommendation: Towards next-generation recommender paradigm,

    W. Wang, X. Lin, F. Feng, X. He, and T.-S. Chua, “Generative rec- ommendation: Towards next-generation recommender paradigm,” arXiv preprint arXiv:2304.03516, 2023

    arXiv 2023

  2. [2]

    Onerec: Unifying retrieve and rank with generative recommender and iterative preference alignment,

    J. Deng, S. Wang, K. Cai, L. Ren, Q. Hu, W. Ding, Q. Luo, and G. Zhou, “Onerec: Unifying retrieve and rank with generative recommender and iterative preference alignment,” arXiv preprint arXiv:2502.18965, 2025

    arXiv 2025

  3. [3]

    Actions speak louder than words: trillion- parameter sequential transducers for generative recommendations,

    J. Zhai, L. Liao, X. Liu, Y . Wang, R. Li, X. Cao, L. Gao, Z. Gong, F. Gu, J. He, Y . Lu, and Y . Shi, “Actions speak louder than words: trillion- parameter sequential transducers for generative recommendations,” in Proceedings of the 41st International Conference on Machine Learning , ser. ICML’24. JMLR.org, 2024

    work page 2024

  4. [4]

    Pinrec: Outcome-conditioned, multi-token generative retrieval for industry-scale recommendation systems,

    A. Badrinath, P. Agarwal, L. Bhasin, J. Yang, J. Xu, and C. Rosenberg, “Pinrec: Outcome-conditioned, multi-token generative retrieval for industry-scale recommendation systems,” arXiv preprint arXiv:2504.10507, 2025

    arXiv 2025

  5. [5]

    Mtgr: Industrial-scale generative recommendation framework in meituan,

    R. Han, B. Yin, S. Chen, H. Jiang, F. Jiang, X. Li, C. Ma, M. Huang, X. Li, C. Jing et al., “Mtgr: Industrial-scale generative recommendation framework in meituan,” arXiv preprint arXiv:2505.18654 , 2025

    arXiv 2025

  6. [6]

    Killing two birds with one stone: Unifying retrieval and ranking with a single generative recommendation model,

    L. Zhang, K. Song, Y . Q. Lee, W. Guo, H. Wang, Y . Li, H. Guo, Y . Liu, D. Lian, and E. Chen, “Killing two birds with one stone: Unifying retrieval and ranking with a single generative recommendation model,” arXiv preprint arXiv:2504.16454 , 2025

    arXiv 2025

  7. [7]

    Autoregressive image generation using residual quantization,

    D. Lee, C. Kim, S. Kim, M. Cho, and W.-S. Han, “Autoregressive image generation using residual quantization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 11 523–11 532

    work page 2022

  8. [8]

    Recommender systems with generative retrieval,

    S. Rajput, N. Mehta, A. Singh, R. Keshavan, T. Vu, L. Heidt, L. Hong, Y . Tay, V . Q. Tran, J. Samost, M. Kula, E. H. Chi, and M. Sathiamoorthy, “Recommender systems with generative retrieval,” in Proceedings of the 37th International Conference on Neural Information Processing Systems, ser. NIPS ’23. Red Hook, NY , USA: Curran Associates Inc., 2023, pp. ...

    work page 2023

Show all 45 references
  1. [9]

    Learnable item tokenization for generative recommendation,

    W. Wang, H. Bao, X. Lin, J. Zhang, Y . Li, F. Feng, S.-K. Ng, and T.-S. Chua, “Learnable item tokenization for generative recommendation,” in Proceedings of the 33rd ACM International Conference on Information and Knowledge Management , ser. CIKM ’24. New York, NY , USA: Assoc...

    2024

  2. [10]

    Generative recommender with end-to-end learnable item tokenization,

    E. Liu, B. Zheng, C. Ling, L. Hu, H. Li, and W. X. Zhao, “Generative recommender with end-to-end learnable item tokenization,” in Proceed- ings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval , ser. SIGIR ’25. New York, NY , ...

    2025

  3. [11]

    Generative retrieval with semantic tree-structured identifiers and contrastive learning,

    Z. Si, Z. Sun, J. Chen, G. Chen, X. Zang, K. Zheng, Y . Song, X. Zhang, J. Xu, and K. Gai, “Generative retrieval with semantic tree-structured identifiers and contrastive learning,” in Proceedings of the 2024 Annual International ACM SIGIR Conference on Research and Developmen...

    2024

  4. [12]

    Tokenrec: learning to tok- enize id for llm-based generative recommendation,

    H. Qu, W. Fan, Z. Zhao, and Q. Li, “Tokenrec: learning to tok- enize id for llm-based generative recommendation,” arXiv preprint arXiv:2406.10450, 2024

    2024 arXiv

  5. [13]

    Exploring the limits of transfer learning with a unified text-to-text transformer,

    C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y . Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” Journal of Machine Learning Research, vol. 21, no. 140, pp. 1–67, 2020

    2020

  6. [14]

    Eager: Two-stream generative recommender with behavior-semantic collaboration,

    Y . Wang, J. Xun, M. Hong, J. Zhu, T. Jin, W. Lin, H. Li, L. Li, Y . Xia, Z. Zhao, and Z. Dong, “Eager: Two-stream generative recommender with behavior-semantic collaboration,” in Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , ser. KDD ’...

    2024

  7. [15]

    Content-based collaborative generation for recommender systems,

    Y . Wang, Z. Ren, W. Sun, J. Yang, Z. Liang, X. Chen, R. Xie, S. Yan, X. Zhang, P. Ren, Z. Chen, and X. Xin, “Content-based collaborative generation for recommender systems,” in Proceedings of the 33rd ACM International Conference on Information and Knowledge Management , ser....

    2024

  8. [16]

    Progressive collaborative and semantic knowledge fusion for generative recommendation,

    L. Xiao, H. Wang, C. Wang, L. Ji, Y . Wang, J. Zhu, Z. Dong, R. Zhang, and R. Li, “Progressive collaborative and semantic knowledge fusion for generative recommendation,” arXiv preprint arXiv:2502.06269 , 2025

    2025

  9. [17]

    Sc-rec: Enhancing generative retrieval with self-consistent reranking for sequential recom- mendation,

    T. Kim, S. Yoon, S. Kang, J. Yeo, and D. Lee, “Sc-rec: Enhancing generative retrieval with self-consistent reranking for sequential recom- mendation,” arXiv preprint arXiv:2408.08686 , 2024

    2024 arXiv

  10. [18]

    Eager-llm: Enhancing large language models as recommenders through exogenous behavior- semantic integration,

    M. Hong, Y . Xia, Z. Wang, J. Zhu, Y . Wang, S. Cai, X. Yang, Q. Dai, Z. Dong, Z. Zhang, and Z. Zhao, “Eager-llm: Enhancing large language models as recommenders through exogenous behavior- semantic integration,” in Proceedings of the ACM on Web Conference 2025, ser. WWW ’25. ...

    2025

  11. [19]

    Empowering sequential recommendation from collabora- tive signals and semantic relatedness,

    M. Cheng, H. Zhang, Q. Liu, F. Yuan, Z. Li, Z. Huang, E. Chen, J. Zhou, and L. Li, “Empowering sequential recommendation from collabora- tive signals and semantic relatedness,” in International Conference on Database Systems for Advanced Applications. Springer, 2024, pp. 196– 211

    2024

  12. [20]

    Self-attentive sequential recommenda- tion,

    W.-C. Kang and J. McAuley, “Self-attentive sequential recommenda- tion,” in 2018 IEEE international conference on data mining (ICDM) , IEEE. IEEE Computer Society, 2018, pp. 197–206

    2018

  13. [21]

    Norm of word embedding encodes information gain,

    M. Oyama, S. Yokoi, and H. Shimodaira, “Norm of word embedding encodes information gain,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , H. Bouamor, J. Pino, and K. Bali, Eds. Singapore: Association for Computational Linguistics, De...

    2023

  14. [22]

    Contrastive learning- based sentence encoders implicitly weight informative words,

    H. Kurita, G. Kobayashi, S. Yokoi, and K. Inui, “Contrastive learning- based sentence encoders implicitly weight informative words,” in Find- ings of the Association for Computational Linguistics: EMNLP 2023 , H. Bouamor, J. Pino, and K. Bali, Eds. Singapore: Association for C...

    2023

  15. [23]

    Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering,

    R. He and J. McAuley, “Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering,” in Proceedings of the 25th International Conference on World Wide Web , ser. WWW ’16. Republic and Canton of Geneva, CHE: International World Wide Web...

    2016

  16. [24]

    Justifying recommendations using distantly-labeled reviews and fine-grained aspects,

    J. Ni, J. Li, and J. McAuley, “Justifying recommendations using distantly-labeled reviews and fine-grained aspects,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Pro- cessing and the 9th International Joint Conference on Natural Language Proce...

    2019

  17. [25]

    S3-rec: Self-supervised learning for sequential recom- mendation with mutual information maximization,

    K. Zhou, H. Wang, W. X. Zhao, Y . Zhu, S. Wang, F. Zhang, Z. Wang, and J.-R. Wen, “S3-rec: Self-supervised learning for sequential recom- mendation with mutual information maximization,” in Proceedings of the 29th ACM International Conference on Information & Knowledge Managem...

    2020

  18. [26]

    Matrix factorization techniques for recommender systems,

    Y . Koren, R. Bell, and C. V olinsky, “Matrix factorization techniques for recommender systems,” Computer, vol. 42, no. 8, pp. 30–37, 2009

    2009

  19. [27]

    Lightgcn: Simplifying and powering graph convolution network for recommenda- tion,

    X. He, K. Deng, X. Wang, Y . Li, Y . Zhang, and M. Wang, “Lightgcn: Simplifying and powering graph convolution network for recommenda- tion,” in Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval , ser. SIGIR ’20. Ne...

    2020

  20. [28]

    Personalized top-n sequential recommendation via convolutional sequence embedding,

    J. Tang and K. Wang, “Personalized top-n sequential recommendation via convolutional sequence embedding,” in Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining , ser. WSDM ’18. New York, NY , USA: Association for Computing Machinery, 2018, p...

    2018

  21. [29]

    Hierarchical gating networks for sequential recommendation,

    C. Ma, P. Kang, and X. Liu, “Hierarchical gating networks for sequential recommendation,” in Proceedings of the 25th ACM SIGKDD Interna- tional Conference on Knowledge Discovery & Data Mining , ser. KDD ’19. New York, NY , USA: Association for Computing Machinery, 2019, p. 825–833

    2019

  22. [30]

    How to index item ids for recommendation foundation models,

    W. Hua, S. Xu, Y . Ge, and Y . Zhang, “How to index item ids for recommendation foundation models,” in Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region , ser. SIGIR-AP ’23. New York, NY...

    2023

  23. [31]

    Llama 2: Open foundation and fine-tuned chat models,

    H. Touvron, L. Martin, K. Stone, P. Albert, A. Almahairi, Y . Babaei, N. Bashlykov, S. Batra, P. Bhargava, S. Bhosale et al. , “Llama 2: Open foundation and fine-tuned chat models,” arXiv preprint arXiv:2307.09288, 2023

    2023 arXiv

  24. [32]

    Decoupled weight decay regularization,

    I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019 . OpenReview.net, 2019

    2019

  25. [33]

    Fusing similarity models with markov chains for sparse sequential recommendation,

    R. He and J. McAuley, “Fusing similarity models with markov chains for sparse sequential recommendation,” in 2016 IEEE 16th International Conference on Data Mining (ICDM) , 2016, pp. 191–200

    2016

  26. [34]

    Factorizing personalized markov chains for next-basket recommendation,

    S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme, “Factorizing personalized markov chains for next-basket recommendation,” in Pro- ceedings of the 19th International Conference on World Wide Web , ser. WWW ’10. New York, NY , USA: Association for Computing Machinery, 2010, p...

  27. [35]

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

    B. Hidasi and A. Karatzoglou, “Recurrent neural networks with top- k gains for session-based recommendations,” in Proceedings of the 27th ACM International Conference on Information and Knowledge Management, ser. CIKM ’18. New York, NY , USA: Association for Computing Machiner...

    2018

  28. [36]

    Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer,

    F. Sun, J. Liu, J. Wu, C. Pei, X. Lin, W. Ou, and P. Jiang, “Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer,” in Proceedings of the 28th ACM International Con- ference on Information and Knowledge Management , ser. CIKM ’19. New...

    2019

  29. [37]

    Feature-level deeper self-attention network for sequential recommendation,

    T. Zhang, P. Zhao, Y . Liu, V . S. Sheng, J. Xu, D. Wang, G. Liu, and X. Zhou, “Feature-level deeper self-attention network for sequential recommendation,” in Proceedings of the 28th International Joint Con- ference on Artificial Intelligence , ser. IJCAI’19. AAAI Press, 2019,...

    2019

  30. [38]

    Actionpiece: Contextually tokeniz- ing action sequences for generative recommendation,

    Y . Hou, J. Ni, Z. He, N. Sachdeva, W.-C. Kang, E. H. Chi, J. McAuley, and D. Z. Cheng, “Actionpiece: Contextually tokeniz- ing action sequences for generative recommendation,” arXiv preprint arXiv:2502.13581, 2025

    2025 arXiv

  31. [39]

    Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5),

    S. Geng, S. Liu, Z. Fu, Y . Ge, and Y . Zhang, “Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5),” in Proceedings of the 16th ACM Conference on Recommender Systems , ser. RecSys ’22. New York, NY , USA: Association fo...

    2022

  32. [40]

    Idgenrec: Llm- recsys alignment with textual id learning,

    J. Tan, S. Xu, W. Hua, Y . Ge, Z. Li, and Y . Zhang, “Idgenrec: Llm- recsys alignment with textual id learning,” in Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval , ser. SIGIR ’24. New York, NY , USA: Association...

    2024

  33. [41]

    Adapting large language models by integrating collaborative semantics for recommendation,

    B. Zheng, Y . Hou, H. Lu, Y . Chen, W. X. Zhao, M. Chen, and J.-R. Wen, “Adapting large language models by integrating collaborative semantics for recommendation,” in 2024 IEEE 40th International Conference on Data Engineering (ICDE) . IEEE, 2024, pp. 1435–1448

    2024

  34. [42]

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

    K. Bao, J. Zhang, Y . Zhang, W. Wang, F. Feng, and X. He, “Tallrec: An effective and efficient tuning framework to align large language model with recommendation,” in Proceedings of the 17th ACM Conference on Recommender Systems , ser. RecSys ’23. New York, NY , USA: Associati...

    2023

  35. [43]

    A bi-step grounding paradigm for large language models in recommendation systems,

    K. Bao, J. Zhang, W. Wang, Y . Zhang, Z. Yang, Y . Luo, C. Chen, F. Feng, and Q. Tian, “A bi-step grounding paradigm for large language models in recommendation systems,” ACM Trans. Recomm. Syst. , vol. 3, no. 4, Apr. 2025

    2025

  36. [44]

    Collm: Integrating collaborative embeddings into large language models for recommendation,

    Y . Zhang, F. Feng, J. Zhang, K. Bao, Q. Wang, and X. He, “Collm: Integrating collaborative embeddings into large language models for recommendation,” IEEE Transactions on Knowledge and Data Engi- neering, vol. 37, no. 5, pp. 2329–2340, 2025

    2025

  37. [45]

    Llara: Large language-recommendation assistant,

    J. Liao, S. Li, Z. Yang, J. Wu, Y . Yuan, X. Wang, and X. He, “Llara: Large language-recommendation assistant,” in Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval , ser. SIGIR ’24. New York, NY , USA: Association ...

    2024

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.