Pith. sign in

REVIEW 4 major objections 5 minor 32 references

Efficient and Effective Query Context-Aware Learning-to-Rank Model for Sequential Recommendation

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read Query context in the last attention layer improves recommendations

desk verdict A real online A/B lift for query context is the paper's solid core, but the open-dataset experiments leak the target label by using next-item category as the query context, so Approach C's offline edge and the RQ2 conclusion don't survive scrutiny. read the letter →

arxiv 2507.03789 v2 pith:2RWLQZK5 submitted 2025-07-04 cs.IR cs.LG

Andrii Dzhoha , Alisa Mironenko , Evgeny Labzin , Vladimir Vlasov , Maarten Versteegh , Marjan Celikik This is my paper
classification cs.IRcs.LG
keywords sequentialrecommendationquerycontexttransformerlearning-to-rankcausallanguagemodelingtraining-servingmismatchtwo-towermodeldiversity
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 asks how a transformer-based sequential recommender can use query context - the category or search query under which a user is browsing - to predict the next item, when historical query context is unavailable at serving time. The authors compare three integration strategies and find that adding the shifted query context only at the query position of the last attention layer, plus adding it to the output scoring head, matches or exceeds the other placements while fully avoiding the training-serving mismatch. This placement, called Approach C, also avoids representing past items with information about later queries. The paper reports offline gains in recall and NDCG on a large-scale production ranking system and on open datasets, and an online A/B test of the input-side variant improved engagement, revenue, and recommendation diversity.

What carries the argument

The object that carries the argument is the shifted query-context embedding $\boldsymbol{L}\boldsymbol{C}$, where $\boldsymbol{L}$ is an $N \times (N+1)$ subdiagonal shift matrix bringing the context of the next item $c_{i+1}$ to position $i$. Approach C adds this term to the query projection of the last transformer layer, and adds the same shifted term to the output logits, while leaving all keys and values clean. That placement is what allows the model to be trained with historical context and served with only the current context, since no future information ever enters the hidden-state stream.

What would settle it

Train Approach C on an open dataset where the query context is an actually logged browsing category or search query that precedes the interaction and is not the target item's category, then compare with a no-context model and with the next-item-category proxy. If the real-context gains fall to zero or below the proxy-based gains, the central claim that fusing query context at the last-layer query position drives the improvement is not supported.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central claim is that query context should enter a causal transformer at exactly one controlled point: the query projection of the last layer, together with the output head. Writing the shifted context matrix as $\boldsymbol{L}\boldsymbol{C}$ with $\boldsymbol{L}$ a subdiagonal shift, the method uses $\boldsymbol{Q}^{(H)} = (\boldsymbol{X}^{(H)} + \boldsymbol{L}\boldsymbol{C})\boldsymbol{W}^{(H)}_Q$ while leaving $\boldsymbol{K}^{(H)}$ and $\boldsymbol{V}^{(H)}$ unmodified; the same shifted context is added to the logits. The paper argues this fuses past items with the current query context inside attention without propagating next-item information through earlier layers or through keys and values, and it reports offline uplifts on the production Browse and Search ranking use cases (up to +6.56% NDCG@500 for Browse) and online gains in engagement, orders, and diversity, with the A/B test of the input-side variant showing +1.6% engagement and +0.7% revenue. On Taobao and Retailrocket, Approach C is within a small margin of the best input-side variant while remaining immune to the training-serving mismatch.

Load-bearing premise

The open-dataset experiments use the category of the next item as the query context, but that category is the very label the model is trained to predict, so using it as input leaks the answer; if this proxy is not a faithful substitute for a genuinely observed query context, the open-dataset comparisons do not support the paper's conclusions.

Editorial extensions

If this is right

  • Using query context as a separate feature (Approach A) already improves Recall@500 and NDCG@500 in the production Browse and Search models, but fusing it inside attention (Approaches B and C) yields larger gains.
  • Approach C reaches production offline uplifts of +4.66% Recall@500 and +4.65% NDCG@500 for Browse and +2.32% and +3.04% for Search, matching the best input-side variant while avoiding the training-serving mismatch.
  • On Taobao and Retailrocket, optimized Approach B (masking rate 0.25) and Approach C are close in quality, and both outperform no-mask input fusion when only current context is available at inference.
  • Historical query context at inference did not improve results over current-context-only serving in either the open datasets or the production setup, so the paper concludes it may not be essential.
  • The online A/B test of the input-side variant improved engagement by +1.6% with 95% CI [1.4, 1.9]%, revenue by +0.7% [0.3, 1.2]%, and cut impressions on non-diverse pages by 66%.

Reading between the lines

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

  • In our reading, the next-item-category proxy is the vulnerable spot: it makes the query context informative by construction, so the open-dataset deltas should be re-checked with a logged query that precedes the interaction.
  • The last-layer query-position idea is a general recipe for any causal transformer that must consume an action-level context at scoring time, not only recommenders; it may transfer to other sequence labeling and ranking tasks with a final-step context.
  • Because Approach C needs no historical context at serving, it could sidestep privacy and feature-store constraints in production without architectural surgery; this is a consequence the paper states qualitatively but does not quantify.
  • A natural extension is to feed item-category embeddings at the input layer (as the paper itself suggests) to recover the historical-context sequence signal while still using Approach C for the current context.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper studies how to incorporate query context (e.g., browse category) into a transformer-based sequential recommender trained with causal language modeling. It identifies a temporal misalignment: the query context for the next item is not aligned with the item sequence at the position where the loss is computed. Three integration strategies are compared: adding the context as a separate feature outside the transformer (Approach A), adding shifted context embeddings to the first-layer input with random masking (Approach B), and adding shifted context embeddings only to the last layer's query position plus the output head (Approach C). The paper reports offline and online experiments on a large e-commerce platform and on two open datasets (Taobao, Retailrocket). The main positive evidence is a statistically significant online A/B test for Approach B against a no-context baseline. The paper argues that Approach C achieves similar gains while avoiding training-serving mismatch and misleading representations, and it concludes from open-dataset results that historical query context at inference is not essential.

Significance. If the central claims were fully supported, the paper would be a useful practical contribution: it identifies a real training-serving mismatch in fusing query context into transformer recommenders, proposes a computationally efficient integration point, and ships open-source code enabling reproduction. The reported online A/B test for Approach B is a genuine strength; it has confidence intervals and multiple engagement and revenue metrics. However, the open-dataset evaluation is undermined by a label-leakage problem: the query context is defined as the next-item category, which is the target label of the prediction task. This makes the offline comparisons in Tables 3 and 4 uninterpretable as evidence about query-context integration. The paper's central claim that Approach C improves ranking quality beyond baseline and beyond simpler context additions is not established by the provided evidence, because the online C-versus-B comparison was not statistically significant and the proprietary offline results lack error bars.

major comments (4)
  1. [Section 7, Methodology] The open-dataset evaluation leaks the target label. The paper states 'We use the next-item category as the query context.' For a prediction at position i, the model is trained and evaluated to predict item x_{i+1}, and the query context c_{i+1} is exactly the category of that target item. Thus the model is given the target's category as an input before ranking, making the prediction task artificially easy. This invalidates the relative comparisons in Table 3 between Approaches A, B, and C, because any observed gains may simply reflect how effectively each architecture exploits the leaked category rather than how well it integrates genuine query context. The masking-rate trend (optimal masking 0.25) is also confounded: partial masking can regularize a degenerate shortcut, so the concavity is not evidence about query-context masking in deployment. The statement in Section 7 that a no-context model is not included because it would be 'inherently disadvantaged in this setting' confirms that the setting itself embeds the label into the input.
  2. [Section 7, Table 4] The conclusion that historical query context is not essential (RQ2) is especially fragile because of the same label leakage. If the current query context already equals the target item's category, historical categories add little information by construction; the null result in Table 4 is therefore not informative about whether historical query context matters when query context is a genuinely observed, non-label signal. This conclusion is load-bearing for the paper's stated RQ2 and for the practical guidance that historical context need not be stored or served.
  3. [Section 6.1, Table 1] The proprietary offline results in Table 1 are reported only as relative uplifts without confidence intervals, error bars, or significance tests. The differences between Approach C and Approach B are small in several rows (e.g., Browse Recall@500 +4.66% vs +4.69%, Search Recall@500 +2.32% vs +2.27%), so the reader cannot determine whether the reported ordering is noise. This matters because the paper uses Table 1 to argue that Approach C is the best-performing method.
  4. [Section 6.2, Online experiments] The online A/B test provides statistically significant evidence only for Approach B against the no-context baseline. The paper explicitly states that testing Approach C against the new winner, Approach B, 'did not yield statistically significant improvements in the main metrics.' Therefore the core claim that Approach C improves ranking quality beyond a simpler approach that adds context to the final score is not supported by the online data. The paper's case for Approach C rests on its architectural properties and on the flawed open-dataset comparisons, not on a significant head-to-head result.
minor comments (5)
  1. [Section 5.3] The sentence 'the projections in the previous layers,1 = 2,...,H -1' contains a typo; it should read 'h = 2, ..., H-1'.
  2. [Section 3.2, Loss equation] In the categorical cross-entropy loss, the term y^u_{t,x^u_{i+1}} uses an undefined index t; it should be y^u_{i,x^u_{i+1}} for consistency with the definition of y^u_{i,j}.
  3. [Section 7, Table 3] The caption says the baseline is indicated by underline, but the table as rendered does not visually underline the row 'In the input without masking'. Please make the baseline formatting explicit or state the baseline in the caption.
  4. [Section 7, first paragraph] The reference to 'contextual information' in the Taobao and Retailrocket datasets is imprecise: the datasets contain item categories rather than a recorded query or browse category chosen by the user. The approximation is stated, but the distinction between item category and observed query context should be emphasized earlier and more prominently.
  5. [Section 6, Dataset description] The negative sampling ratio is given as 0.5% of all classes with no further detail on how it interacts with the sampled softmax; a sentence on the sampling distribution or a reference to the standard log-uniform sampler would improve reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central claims are empirical and benchmarked externally; the co-authored citation [22] is supporting, not load-bearing, and the open-dataset label-proxy issue is a validity threat, not a circular derivation.

full rationale

The paper's central claims are empirical rather than derivational. Approach C is defined by adding shifted query-context embeddings to the last layer's query projection and to the output head (Section 5.3 and Eq. 5); its improvement over Approaches A and B is tested on proprietary and open datasets and via an A/B test, so the headline result is not equal to an input by construction. The query-context masking rate (0.25) is grid-searched and reported as a configuration, not fitted and then renamed as a prediction. The only self-referential element is the design principle in Section 5.3 citing the co-authored [22], but the same section supplies an independent attention-flow argument and the approach is externally benchmarked, so the citation is not load-bearing. The open-dataset methodology uses the next-item category as the query context, which can leak the target label and weaken the offline comparisons; that is a validity and leakage concern, not a circularity of the kind where an equation reduces to its input by construction or a fitted parameter is relabeled as a prediction. Under the defined patterns, no circular step is established.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim rests on standard transformer machinery plus a domain assumption about inference-time context availability. No new particles, mediators, forces, or dimensions are introduced. The main non-standard assumptions are the label-derived query-context proxy in the open datasets and the self-cited design principle for Approach C.

free parameters (3)
  • Query context masking rate (Approach B) = 0.3 (Zalando), 0.25 (Taobao/Retailrocket), with 0.5 and 0.75 also grid-searched
    Chosen by grid search on offline holdout data; the best masking rate is a fitted value that directly controls the method's behaviour and the reported gains.
  • Negative sampling ratio = 0.5% of all classes
    Reported as fine-tuned offline to balance training efficiency and effectiveness (Section 6); it influences training but is not the main claim.
  • Number of transformer layers and heads = 2 blocks/4 heads (Zalando), 2 layers/2 heads (open datasets)
    Hyperparameters chosen without reported sensitivity analysis; they affect all approaches equally.
assumptions (4)
  • standard math Transformer self-attention equations, causal masking, and sampled softmax loss are valid modeling choices.
    The method builds on SASRec-style transformer training (Section 3.2) and uses standard attention and loss formulations.
  • domain assumption Only the current query context is available at inference; historical query context is unavailable due to privacy and feature-store constraints.
    This is the motivating assumption of the paper (Section 1) and is required for the training-serving mismatch discussion and the RQ2 analysis.
  • ad hoc to paper The next-item category is a valid proxy for the query context in Taobao and Retailrocket.
    Section 7 states 'We use the next-item category as the query context' because the datasets do not provide real query categories. This assumption is load-bearing and leaks target-label information.
  • ad hoc to paper Placing query context in the last layer's query position is the safest way to avoid training-serving mismatch.
    Section 5.3 cites the authors' own prior work [22] for this design principle; no formal proof or independent verification is given in this paper.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Efficient and Effective Query Context-Aware Learning-to-Rank Model for Sequential Recommendation." pith.science (2026). https://pith.science/paper/2RWLQZK5

@misc{pith2026250703789,
  author       = {Pith},
  title        = {Pith review of: Efficient and Effective Query Context-Aware Learning-to-Rank Model for Sequential Recommendation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2RWLQZK5}},
  note         = {Machine review of arXiv:2507.03789}
}
read the original abstract

Modern sequential recommender systems commonly use transformer-based models for next-item prediction. While these models demonstrate a strong balance between efficiency and quality, integrating interleaving features - such as the query context (e.g., browse category) under which next-item interactions occur - poses challenges. Effectively capturing query context is crucial for refining ranking relevance and enhancing user engagement, as it provides valuable signals about user intent within a session. Unlike item features, historical query context is typically not aligned with item sequences and may be unavailable at inference due to privacy constraints or feature store limitations - making its integration into transformers both challenging and error-prone. This paper analyzes different strategies for incorporating query context into transformers trained with a causal language modeling procedure as a case study. We propose a new method that effectively fuses the item sequence with query context within the attention mechanism. Through extensive offline and online experiments on a large-scale online platform and open datasets, we present evidence that our proposed method is an effective approach for integrating query context to improve model ranking quality in terms of relevance and diversity.

Figures

Figures reproduced from arXiv: 2507.03789 by the authors.

Figure 1
Figure 1. A user’s journey is defined by the items they inter [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. High-level architecture of the transformer with [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. An illustration of the attention flow among posi [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Approach C integrates query context into the trans [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

32 extracted references · 8 canonical work pages

  1. [22]

    Vladimir Vlasov. 2024. Efficient slot labelling. arXiv:2401.09343 [cs.CL] https: //arxiv.org/abs/2401.09343

    work page Pith review arXiv 2024

  2. [1]

    Gediminas Adomavicius and Alexander Tuzhilin. 2015. Context-A ware Recom- mender Systems. Springer US, Boston, MA, 191–226. doi:10.1007/978-1-4899- 7637-6_6

    doi:10.1007/978-1-4899- 2015

  3. [2]

    Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016. Layer normaliza- tion. arXiv preprint arXiv:1607.06450 (2016)

    arXiv 2016

  4. [3]

    Alex Beutel, Paul Covington, Sagar Jain, Can Xu, Jia Li, Vince Gatto, and Ed H. Chi. 2018. Latent Cross: Making Use of Context in Recurrent Recommender Systems. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining (Marina Del Rey, CA, USA) (WSDM ’18). Association for Computing Machinery, New York, NY, USA, 46–54. doi:1...

    work page arXiv 2018

  5. [4]

    Renqin Cai, Jibang Wu, Aidan San, Chong Wang, and Hongning Wang. 2021. Category-aware Collaborative Sequential Recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Infor- mation Retrieval (Virtual Event, Canada) (SIGIR ’21). Association for Computing Machinery, New York, NY, USA, 388–397. doi:10.1145...

    arXiv 2021

  6. [5]

    Huanhuan Cao, Daxin Jiang, Jian Pei, Qi He, Zhen Liao, Enhong Chen, and Hang Li. 2008. Context-aware query suggestion by mining click-through and session data. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Las Vegas, Nevada, USA) (KDD ’08). Association for Computing Machinery, New York, NY, USA, 875...

    arXiv 2008

  7. [6]

    Marjan Celikik, Jacek Wasilewski, Ana Peleteiro Ramallo, Alexey Kurennoy, Evgeny Labzin, Danilo Ascione, Tural Gurbanov, Géraud Le Falher, Andrii Dzhoha, and Ian Harris. 2024. Building a Scalable, Effective, and Steerable Search and Ranking Platform. arXiv:2409.02856 [cs.IR] https://arxiv.org/abs/2409.02856

    arXiv 2024

  8. [7]

    Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems (Boston, Massachusetts, USA) (RecSys ’16). Association for Computing Machinery, New York, NY, USA, 191–198. doi:10.1145/2959100. 2959190

    doi:10.1145/2959100 2016

Show all 32 references
  1. [8]

    Giulia Di Teodoro, Federico Siciliano, Nicola Tonellotto, and Fabrizio Silvestri

  2. [9]

    Andrii Dzhoha, Alexey Kurennoy, Vladimir Vlasov, and Marjan Celikik. 2024. Re- ducing Popularity Influence by Addressing Position Bias. arXiv:2412.08780 [cs.IR] https://arxiv.org/abs/2412.08780

    2024 arXiv

  3. [10]

    Wang-Cheng Kang and Julian McAuley. 2018. Self-Attentive Sequential Recom- mendation. 2018 IEEE International Conference on Data Mining (ICDM) (2018), 197–206

    2018

  4. [11]

    Woo-Seung Kang, Hye-Jin Jeong, Suwon Lee, and Sang-Min Choi. 2024. Indepen- dent Representation of Side Information for Sequential Recommendation. IEEE Access 12 (2024), 148516–148524. doi:10.1109/ACCESS.2024.3476976

    2024

  5. [12]

    Jiacheng Li, Yujie Wang, and Julian McAuley. 2020. Time Interval Aware Self- Attention for Sequential Recommendation. InProceedings of the 13th International Conference on Web Search and Data Mining (Houston, TX, USA) (WSDM ’20). Association for Computing Machinery, New York, ...

    2020

  6. [13]

    Li Erran Li, Eric Chen, Jeremy Hermann, Pusheng Zhang, and Luming Wang. 2017. Scaling Machine Learning as a Service. In Proceedings of The 3rd International Conference on Predictive Applications and APIs (Proceedings of Machine Learning Research, Vol. 67), Claire Hardgrove, Lo...

    2017

  7. [14]

    Qiang Liu, Shu Wu, Diyi Wang, Zhaokang Li, and Liang Wang. 2016. Context- Aware Sequential Recommendation. In 2016 IEEE 16th International Conference on Data Mining (ICDM) . 1053–1058. doi:10.1109/ICDM.2016.0135

    2016

  8. [15]

    Zihan Liu, Yupeng Hou, and Julian McAuley. 2024. Multi-Behavior Generative Recommendation. (2024). arXiv:2405.16871 [cs.IR] https://arxiv.org/abs/2405. 16871

    2024 arXiv

  9. [16]

    Toan Q Nguyen and Julian Salazar. 2019. Transformers without tears: Improving the normalization of self-attention. arXiv preprint arXiv:1910.05895 (2019)

    2019 arXiv

  10. [17]

    Massimo Quadrana, Paolo Cremonesi, and Dietmar Jannach. 2018. Sequence- Aware Recommender Systems. ACM Comput. Surv. 51, 4, Article 66 (July 2018), 36 pages. doi:10.1145/3190616

    2018 doi

  11. [18]

    Jun Song, Jun Xiao, Fei Wu, Haishan Wu, Tong Zhang, Zhongfei Mark Zhang, and Wenwu Zhu. 2017. Hierarchical Contextual Attention Recurrent Neural Network for Map Query Suggestion. IEEE Transactions on Knowledge and Data Engineering 29, 9 (2017), 1888–1901. doi:10.1109/TKDE.2017.2700392

    2017

  12. [19]

    Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang

  13. [20]

    Yan-Martin Tamm, Rinchin Damdinov, and Alexey Vasilev. 2021. Quality Metrics in Recommender Systems: Do We Calculate Metrics Consistently?. InProceedings of the 15th ACM Conference on Recommender Systems (Amsterdam, Netherlands) (RecSys ’21). Association for Computing Machiner...

    2021

  14. [21]

    Gomez, Łukasz Kaiser, and Illia Polosukhin

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems (Long Beach, California, ...

    2017

  15. [23]

    Xue Xia, Pong Eksombatchai, Nikil Pancha, Dhruvil Deven Badani, Po-Wei Wang, Neng Gu, Saurabh Vishwas Joshi, Nazanin Farahpour, Zhiyuan Zhang, and An- drew Zhai. 2023. TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest. In Proceedings of the...

    2023

  16. [24]

    Yueqi Xie, Peilin Zhou, and Sunghun Kim. 2022. Decoupled Side Information Fusion for Sequential Recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (Madrid, Spain) (SIGIR ’22). Association for Compu...

    2022

  17. [25]

    Wong, Shuming Shi, and Zhaopeng Tu

    Baosong Yang, Longyue Wang, Derek F. Wong, Shuming Shi, and Zhaopeng Tu

  18. [26]

    Ji Yang, Xinyang Yi, Derek Zhiyuan Cheng, Lichan Hong, Yang Li, Simon Xiaom- ing Wang, Taibai Xu, and Ed H. Chi. 2020. Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations. In Companion Proceedings of the Web Conference 2020 (Taipei, Taiwan)(WWW ’2...

    2020

  19. [27]

    Biao Zhang, Deyi Xiong, Jinsong Su, and Hong Duan. 2017. A Context-Aware Recurrent Encoder for Neural Machine Translation. IEEE/ACM Transactions on Audio, Speech, and Language Processing 25, 12 (2017), 2424–2432. doi:10.1109/ TASLP.2017.2751420

    2017

  20. [28]

    Sheng, Jiajie Xu, Deqing Wang, Guanfeng Liu, and Xiaofang Zhou

    Tingting Zhang, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Deqing Wang, Guanfeng Liu, and Xiaofang Zhou. 2019. Feature-level deeper self-attention network for sequential recommendation. In Proceedings of the 28th International Joint Conference on Artificial Intelli...

    2019

  21. [29]

    Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018. Deep Interest Network for Click- Through Rate Prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining...

    2018

  22. [2019]

    In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (Beijing, China) (CIKM ’19)

    BERT4Rec: Sequential Recommendation with Bidirectional Encoder Rep- resentations from Transformer. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (Beijing, China) (CIKM ’19). Association for Computing Machinery, New York, NY, US...

  23. [2021]

    Neurocomputing 458 (2021), 157–169

    Context-aware Self-Attention Networks for Natural Language Processing. Neurocomputing 458 (2021), 157–169. doi:10.1016/j.neucom.2021.06.009

    2021 doi

  24. [2024]

    arXiv preprint arXiv:2411.07770 (2024)

    A Theoretical Analysis of Recommendation Loss Functions under Negative Sampling. arXiv preprint arXiv:2411.07770 (2024)

    2024 arXiv

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.