Pith. sign in

REVIEW 3 major objections 6 minor 4 cited by

FuXi-$\alpha$: Scaling Recommendation Model with Feature Interaction Enhanced Transformer

T0 review · 3 major / 6 minor · reviewed 2026-08-09 · deepseek-v4-flash

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

Pith's one-line read The paper claims that FuXi-α, an autoregressive recommender whose attention is split into semantic, temporal, and positional channels, beats all tested baselines and keeps improving with scale.

desk verdict FuXi-alpha is a solid sequential-recommendation paper with consistent wins over HSTU and a real deployment, but the headline comparison is not parameter-matched and the scaling-law claim is overstated. read the letter →

arxiv 2502.03036 v1 pith:JIJRUETY submitted 2025-02-05 cs.IR

Yufei Ye , Wei Guo , Jin Yao Chin , Hao Wang , Hong Zhu , Xi Lin , Yuyang Ye , Yong Liu
show 3 more authors
Ruiming Tang Defu Lian Enhong Chen
This is my paper · ORCID
classification cs.IR
keywords sequentialrecommendationgenerativefeatureinteractionscalinglawsadaptivemulti-channelself-attentiontemporalinformationpositionalonlineA/Btesting
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

FuXi-α is an autoregressive sequential recommendation model built on the idea that temporal and positional signals deserve their own attention channels rather than being added into semantic attention weights or blended into latent vectors. The paper's central claim is that this decoupling, together with a two-stage feed-forward network for implicit feature interactions, produces consistent gains: FuXi-α reports the best HR@10/50, NDCG@10/50, and MRR on MovieLens-1M, MovieLens-20M, and KuaiRand against SASRec, LLaMa, HSTU, and older baselines. If true, the result matters because it offers a concrete architectural recipe for scaling sequential recommenders along LLM-style scaling laws, and because a deployed version in Huawei Music raised songs played per user by 4.76% and listening time by 5.10%. The paper frames the model as restoring the balance between explicit and implicit feature interactions that HSTU tipped too far toward the explicit side.

What carries the argument

The load-bearing mechanism is the Adaptive Multi-channel Self-attention (AMS) layer together with the Multi-stage Feed-Forward Network (MFFN). AMS runs three parallel attention channels—semantic (learned query/key/value attention), temporal (bucketed timestamp differences with per-bucket learnable weights), and positional (learned relative-position weights)—shares the value vectors across channels, concatenates the channel outputs, normalizes them, and gates the result by element-wise multiplication with a projection of the layer input. The MFFN first fuses the channel outputs with the layer input and then applies a SwiGLU feed-forward stage for implicit interactions. The formal account is a polynomial-approximation induction: after $b$ stacked FuXi blocks, the output at a position is $x_{0,i} F_{2^b-1}$ with interaction degree up to $2^b-1$, showing why added depth adds expressive power.

What would settle it

Train FuXi-α and HSTU with identical total parameter counts and identical FLOPs—not just identical listed hyperparameters—on MovieLens-20M and KuaiRand and compare NDCG@10; if the margin collapses, the central architectural claim fails.

Watch

Extended reading notes

Core claim

FuXi-α's central discovery is that sequential recommendation improves when self-attention stops folding time and position into semantic attention weights and instead runs three parallel channels—semantic, temporal, and positional—that share value vectors, then fuses them with a two-stage feed-forward network. The paper argues that this restores the implicit feature interactions that HSTU de-emphasized and makes temporal and positional cues more expressive. After $b$ stacked blocks the output is $x_{0,i} F_{2^b-1}$, an interaction polynomial of degree up to $2^b - 1$, which the paper offers as the formal reason performance keeps climbing with depth. In offline evaluation FuXi-α reports the best HR@K, NDCG@K, and MRR on MovieLens-1M, MovieLens-20M, and KuaiRand, and in an online A/B test it reports a 4.76% increase in songs played per user and a 5.10% increase in listening duration.

Load-bearing premise

The comparisons are read as matching HSTU's capacity because the listed parameter settings match, even though the added attention channels add weights that are not counted in that match, so some of the reported gain could be capacity rather than the mechanism.

Editorial extensions

If this is right

  • At two layers FuXi-α beats the strongest baseline by 13.24% in NDCG@10 averaged over the three public datasets; at eight layers the margin over HSTU-Large is 7.26% in NDCG@10.
  • On the industrial music dataset, scaling from 2 to 32 layers improves NDCG@10 and HR@10 monotonically, which the paper reads as adherence to a scaling law.
  • Adding negative samples from 32 to 256 produces larger gains than increasing layers from 2 to 16 on the tested datasets, making sampling ratio a first-order scaling knob.
  • A 7-day A/B test in Huawei Music reports 4.76% more songs played per user and 5.10% more listening time per user relative to a mature multi-channel baseline, after which the model became a permanent channel.
  • FuXi-α achieves throughput comparable to HSTU but lower than SASRec and LLaMa, so the performance gains come with a measurable speed cost.

Reading between the lines

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

  • If the channel separation, not the extra parameters, drives the gains, the AMS design should transfer to other Transformer-based sequence models where timestamp and position are side information.
  • Varying the number of temporal buckets while holding everything else fixed would show how much of the gain depends on time quantization, a lever the paper leaves untested.
  • The polynomial-approximation argument implies scaling gains should saturate once a dataset's required interaction degree is reached; MovieLens-1M already declines at 16 layers, so locating that saturation point on industrial data would test the mechanism's own account.
  • Because negative sampling showed larger returns than depth, the reported scaling law may be confounded with sampling pressure; a joint sweep of data size, negatives, and layers would separate the two.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. The paper introduces FuXi-alpha, an autoregressive sequential recommendation transformer whose FuXi block replaces standard self-attention with an Adaptive Multi-channel Self-attention (AMS) mechanism that routes semantic, temporal, and positional signals through separate channels, and replaces the usual FFN with a two-stage Multi-stage FFN (MFFN) for implicit feature interactions. The authors compare against BPRMF, GRU4Rec, NARM, SASRec, LLaMa, and HSTU on MovieLens-1M, MovieLens-20M, KuaiRand, and a private Huawei Music dataset; report consistent wins across all public datasets and layer depths; report scaling behavior with increasing layer count; and report a 7-day online A/B test in Huawei Music with a 4.76%/4.67% plays-per-user increase and a 5.10% listening-duration increase. Code is released.

Significance. If the empirical claims hold, FuXi-alpha is a strong candidate for industrial sequential recommendation, and the paper provides useful evidence that autoregressive generative recommendation can be improved by more explicit temporal/positional modeling plus additional FFN capacity. The released code, public-benchmark results, and the held-out industrial traffic evaluation are concrete strengths. However, the core attribution claim, that the architecture rather than additional parameters drives the gains, is not established by the reported experiments, and the scaling-law statement is not supported by a fitted law. These issues materially affect the central contribution, so the paper needs a major revision before the claims can be accepted.

major comments (3)
  1. [§6.1.4, §4.2.1–4.2.2, Table 2] The comparison with HSTU is not parameter-matched. The paper states that 'we maintain the same model parameters as HSTU in the first two datasets, except for the number of layers' (§6.1.4), but FuXi-alpha's per-layer parameter count is strictly larger than HSTU's: the AMS adds the projection W_u ∈ R^{d×3d_h} in Eq. (4) plus per-layer relative position/timestamp bucket embeddings, the MFFN adds W_o in Eq. (5) and the SwiGLU matrices W_1, W_2, W_3 in Eq. (7), and §5.4.2 notes that HSTU lacks an FFN layer. Consequently Table 2 and the layer-scaling curves cannot separate the benefit of the AMS/MFFN design from the added capacity, and the ablations in Table 5 have the same confound. Please add parameter-matched comparisons (e.g., matching total non-embedding parameters by adjusting hidden sizes or by adding a comparable FFN to HSTU), or explicitly reposition the results as a capacity-uncontrolled comparison and soften the attribution.
  2. [§6.2.3, §7, Figure 6] The claim that FuXi-alpha 'adheres to the scaling law' is not supported by the evidence. Figure 5 shows only a monotone trend over layer counts 2–32 on the industrial dataset with no fitted power law, no held-out scaling-law analysis in terms of data or compute, and no statistical uncertainty; Figure 6 adds only four points per dataset. More importantly, Figure 6(a) shows that on MovieLens-1M NDCG@10 and HR@10 decline when going from 8 to 16 layers, which directly contradicts the conclusion in §7 that 'the performance continually improves while scaling up our model.' Please either fit and report a specific scaling relation or explicitly state the weaker observed trend, and discuss the MovieLens-1M failure case.
  3. [§5.2, Eqs. (10)–(12)] The polynomial-approximation argument as written is not correct. After substituting the inductive hypothesis into Eq. (10), the expression Σ_j a_{i,j}^{(l+1)} x_{0,j} F_{2^l−1} + 1 has degree at most 2^l, so the claim that every term with total degree up to 2^{l+1} appears in this expression is wrong; the stated equality to F_{2^l} in Eq. (12) needs a degree bound of 2^l, and the subsequent step to degree 2^{l+1}−1 requires a separate argument about the product with the outer F_{2^l−1}. Please correct the induction indexing or state a more limited 'degree at most' result. The proof also relies on the simplified block in Eq. (9), so the conclusion should be framed as an expressiveness statement for the simplified model rather than an exact characterization of the full FuXi block.
minor comments (6)
  1. [Abstract vs. §6.6] The reported online A/B play-count lift is inconsistent: the Abstract and §1 report 4.76%, while §6.6 reports 4.67% for the same metric.
  2. [§6.1.4, Table 2] The text says the deep variants are denoted 'XX-Large' after stacking 4x layers to 8 layers, but Table 2 and the surrounding text use the suffix '-Large'; please align the naming.
  3. [§6.4, Table 5] The variant label 'w/o MFFN' conflicts with the body text, which says that removing only the second stage of the MFFN causes the drop; renaming the variant to 'w/o MFFN-stage-2' would make the ablation unambiguous.
  4. [Figures 5–8, Table 4] The scaling, hyperparameter, and efficiency results report single runs without error bars or variance information; adding standard deviations or at least stating the number of seeds would improve reliability.
  5. [§6.1.1] There are minor language errors, for example 'we process them using a similar manner to HSTU by ourself' and 'users in this platform are usualy very active'; these should be copyedited.
  6. [§5.3, Eq. (16)] The notation in Eq. (16) uses W_o1 and W_o2 as separate output projections for the semantic and positional channels, whereas Eq. (5) defines a single W_o; please clarify whether these are the same weights or a notational simplification.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: FuXi-alpha is validated against external benchmarks, a private industrial dataset, and a held-out online A/B test; the architectural analysis is an inductive degree-bound argument rather than a result defined by its outputs.

full rationale

The central claims are empirical comparisons with external baselines and public/private datasets, not derivations from fitted parameters. Section 4 constructs AMS and MFFN, Section 5.3 compares their algebraic form with additive relative-bias attention, and Section 5.2 proves a degree bound for a simplified block by induction; none of these assume the target outcome. Tables 2-5 and Figure 5 report held-out metrics and ablations, while Section 6.6 uses a 7-day A/B test on 30% of Huawei Music traffic, so the online lift is measured against a deployed baseline rather than defined by the model. Self-citations [14,47,60] motivate the scaling-law framing but are not load-bearing: the paper's own scaling experiments (Figures 5-8) supply the evidence, and [76] is external. Two non-circular issues are noted: the HSTU comparisons in Sections 6.1.4 and 6.2 may not be total-parameter-matched because AMS and MFFN add per-layer parameters, which is a capacity confound rather than a circularity; and the song-play lift is stated as 4.76% in the abstract but 4.67% in Section 6.6, a reporting inconsistency. The polynomial induction also contains a degree-counting slip in Eq. 11, but this is an internal proof error, not circular reasoning.

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

The paper is an empirical ML paper; its central claims rest on benchmark and online data, not on a mathematical derivation with new constants. The only hand-set numbers are standard hyperparameters (hidden dimension, number of layers, negative samples), which are swept in sensitivity studies. The temporal bucketing uses learnable bucket parameters from T5 relative attention, which are part of the model. No new physical constants are introduced.

assumptions (3)
  • domain assumption The AMS decomposition of temporal, positional, and semantic channels improves expressiveness over additive relative biases (Eq. 3 vs Eq. 13-16).
    Section 4.2.1 and Section 5.3 assume separate channels with shared value matrices yield better recommendations; no proof, only empirical gains.
  • ad hoc to paper The polynomial approximation in Section 5.2 assumes attention weights can be treated as constants and omits the MFFN and nonlinearities to prove expressiveness; the induction step as written misstates the degree bound (Eq. 10-12).
    The simplifications are introduced solely to get a closed-form expression for expressiveness; the proof as stated is flawed.
  • domain assumption The scaling-law claim assumes that increasing layer count at fixed other hyperparameters, measured on a private industrial dataset, is a valid proxy for a scaling law (which would require a power-law fit and compute/data controls).
    Section 6.2.3 and Figure 5 draw a scaling-law conclusion from monotonic improvement without fitting a power law or controlling compute budget.

how reviews work

0 comments
Cite this review

Pith. "Pith review of FuXi-$\alpha$: Scaling Recommendation Model with Feature Interaction Enhanced Transformer." pith.science (2026). https://pith.science/paper/JIJRUETY

@misc{pith2026250203036,
  author       = {Pith},
  title        = {Pith review of: FuXi-$\alpha$: Scaling Recommendation Model with Feature Interaction Enhanced Transformer},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JIJRUETY}},
  note         = {Machine review of arXiv:2502.03036}
}
abstract

Inspired by scaling laws and large language models, research on large-scale recommendation models has gained significant attention. Recent advancements have shown that expanding sequential recommendation models to large-scale recommendation models can be an effective strategy. Current state-of-the-art sequential recommendation models primarily use self-attention mechanisms for explicit feature interactions among items, while implicit interactions are managed through Feed-Forward Networks (FFNs). However, these models often inadequately integrate temporal and positional information, either by adding them to attention weights or by blending them with latent representations, which limits their expressive power. A recent model, HSTU, further reduces the focus on implicit feature interactions, constraining its performance. We propose a new model called FuXi-$\alpha$ to address these issues. This model introduces an Adaptive Multi-channel Self-attention mechanism that distinctly models temporal, positional, and semantic features, along with a Multi-stage FFN to enhance implicit feature interactions. Our offline experiments demonstrate that our model outperforms existing models, with its performance continuously improving as the model size increases. Additionally, we conducted an online A/B test within the Huawei Music app, which showed a $4.76\%$ increase in the average number of songs played per user and a $5.10\%$ increase in the average listening duration per user. Our code has been released at https://github.com/USTC-StarTeam/FuXi-alpha.

Figures

Figures reproduced from arXiv: 2502.03036 by the authors.

Figure 1
Figure 1. Different temporal intervals or orders between ob [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 3
Figure 3. Illustration of Adaptive Multi-channel Self [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Diagram of MFFN: Stage 1 fuses outputs from differ [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figures from the paper (3 more)
Figure 5
Figure 5. Figure 5: Scaling of FuXi-𝛼 on Industrial Dataset. by 7.26% in NDCG@10 (5.24% in NDCG@50, 6.14% in HR@10, 3.19% in HR@50, 6.90% in MRR) on average of the three datasets. The excellent performance of FuXi-𝛼 demonstrates the great util￾ity of introducing explicit and implicit feat…
Figure 6
Figure 6. Figure 6: Performances with different number of layers. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 8
Figure 8. Figure 8: Diverse negative sample counts in performances. [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

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

  1. Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks

    cs.LG 2025-09 conditional novelty 6.0 of 10

    A 1-by-1 convolutional autoencoder with six-way softmax outputs jointly predicts interaction likelihood and conditional rating, supported by generalization bounds and mixed but mostly competitive RMSE and Recall resul...

  2. FuXi-\beta: Towards a Lightweight and Fast Large-Scale Generative Recommendation Model

    cs.IR 2025-08 conditional novelty 6.0 of 10

    FuXi-β shows that removing query-key attention and using a functional relative time bias makes generative recommendation Transformers faster and, on industrial datasets, more accurate.

  3. Time to Split: Exploring Data Splitting Strategies for Offline Evaluation of Sequential Recommenders

    cs.IR 2025-07 conditional novelty 6.0 of 10

    Global temporal splits with Last or Random target selection correlate strongly with realistic successive evaluation, while leave-one-out splits produce inconsistent model rankings across datasets.

  4. DLF: Enhancing Explicit-Implicit Interaction via Dynamic Low-Order-Aware Fusion for CTR Prediction

    cs.IR 2025-05 conditional novelty 5.0 of 10

    DLF is a CTR prediction architecture that combines low-rank, high-rank, and implicit interaction blocks with layer-wise attention fusion, reporting state-of-the-art results on Criteo, Avazu, Movielens, and Frappe.

Reference graph

Works this paper leans on

92 extracted references · 21 canonical work pages · cited by 4 Pith papers

  1. [1]

    Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Floren- cia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)

    arXiv 2023

  2. [2]

    Newsha Ardalani, Carole-Jean Wu, Zeliang Chen, Bhargav Bhushanam, and Adnan Aziz. 2022. Understanding scaling laws for recommendation models. arXiv preprint arXiv:2208.08489 (2022)

    arXiv 2022

  3. [3]

    Tom B Brown. 2020. Language models are few-shot learners. arXiv preprint arXiv:2005.14165 (2020)

    arXiv 2020

  4. [4]

    Junyi Chen, Lu Chi, Bingyue Peng, and Zehuan Yuan. 2024. HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modeling. arXiv preprint arXiv:2409.12740 (2024)

    arXiv 2024

  5. [5]

    Xu Chen, Hongteng Xu, Yongfeng Zhang, Jiaxi Tang, Yixin Cao, Zheng Qin, and Hongyuan Zha. 2018. Sequential recommendation with user memory networks. In Proceedings of the eleventh ACM international conference on web search and data mining. 108–116

    2018

  6. [6]

    Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, and Hemal Shah

  7. [7]

    Sharad Chitlangia, Krishna Reddy Kesari, and Rajat Agarwal. 2023. Scaling generative pre-training for user ad activity sequences. (2023)

    2023

  8. [8]

    Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems. 191–198

    2016

Show all 92 references
  1. [9]

    Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdul- mohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme, Matthias Minde...

    2023 arXiv

  2. [10]

    Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024. The llama 3 herd of models. arXiv preprint arXiv:2407.21783 (2024)

    2024 arXiv

  3. [11]

    Stefan Elfwing, Eiji Uchibe, and Kenji Doya. 2018. Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural networks 107 (2018), 3–11

    2018

  4. [12]

    Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N Dauphin

  5. [13]

    Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. arXiv:1703.04247 [cs.IR] https://arxiv.org/abs/1703.04247

    2017 arXiv

  6. [14]

    Wei Guo, Hao Wang, Luankang Zhang, Jin Yao Chin, Zhongzhou Liu, Kai Cheng, Qiushi Pan, Yi Quan Lee, Wanqi Xue, Tingjia Shen, et al . 2024. Scal- ing New Frontiers: Insights into Large Recommendation Models. arXiv preprint arXiv:2412.00714 (2024)

    2024 arXiv

  7. [15]

    Xingzhuo Guo, Junwei Pan, Ximei Wang, Baixu Chen, Jie Jiang, and Mingsheng Long. 2023. On the Embedding Collapse when Scaling up Recommendation Models. arXiv preprint arXiv:2310.04400 (2023)

    2023 arXiv

  8. [16]

    Yongqiang Han, Hao Wang, Kefan Wang, Likang Wu, Zhi Li, Wei Guo, Yong Liu, Defu Lian, and Enhong Chen. 2024. Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation. In Proceedings of the ACM on Web Conference 2024 . 3297–3306

    2024

  9. [17]

    Yongqiang Han, Likang Wu, Hao Wang, Guifeng Wang, Mengdi Zhang, Zhi Li, Defu Lian, and Enhong Chen. 2023. Guesr: A global unsupervised data- enhancement with bucket-cluster sampling for sequential recommendation. In International Conference on Database Systems for Advanced App...

    2023

  10. [18]

    Xiangnan He and Tat-Seng Chua. 2017. Neural Factorization Machines for Sparse Predictive Analytics. arXiv:1708.05027 [cs.LG] https://arxiv.org/abs/1708.05027

    2017 arXiv

  11. [19]

    Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen, Christopher Hesse, Ja- cob Jackson, Heewoo Jun, Tom B Brown, Prafulla Dhariwal, Scott Gray, et al

  12. [20]

    B Hidasi. 2015. Session-based Recommendations with Recurrent Neural Networks. arXiv preprint arXiv:1511.06939 (2015)

    2015 arXiv

  13. [21]

    Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al. 2022. Training compute-optimal large language models. arXiv preprint arXiv:2203.15556 (2022)

    2022 arXiv

  14. [22]

    Jin Huang, Wayne Xin Zhao, Hongjian Dou, Ji-Rong Wen, and Edward Y Chang

  15. [23]

    Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recom- mendation. In 2018 IEEE international conference on data mining (ICDM) . IEEE, 197–206

    2018

  16. [24]

    Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020. Scaling laws for neural language models. arXiv preprint arXiv:2001.08361 (2020)

    2020 arXiv

  17. [25]

    Anton Klenitskiy and Alexey Vasilev. 2023. Turning Dross Into Gold Loss: is BERT4Rec really better than SASRec?. In Proceedings of the 17th ACM Conference on Recommender Systems (RecSys ’23) . ACM, 1120–1125. https://doi.org/10.1145/ 3604915.3610644

    2023

  18. [26]

    John P Kotter. 2012. Accelerate. Harvard business review 90, 11 (2012), 45–58

    2012

  19. [27]

    Honghao Li, Yiwen Zhang, Yi Zhang, Hanwei Li, Lei Sang, and Jieming Zhu

  20. [28]

    Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017. Neural attentive session-based recommendation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management . 1419–1428

    2017

  21. [29]

    Jiacheng Li, Yujie Wang, and Julian McAuley. 2020. Time interval aware self- attention for sequential recommendation. In Proceedings of the 13th international conference on web search and data mining . 322–330

    2020

  22. [30]

    Li Li, Jiawei Peng, Huiyi Chen, Chongyang Gao, and Xu Yang. 2024. How to configure good in-context sequence for visual question answering. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 26710– 26720

    2024

  23. [31]

    Zeyu Li, Wei Cheng, Yang Chen, Haifeng Chen, and Wei Wang. 2020. Interpretable click-through rate prediction through hierarchical attention. In Proceedings of the 13th international conference on web search and data mining . 313–321

    2020

  24. [32]

    Zekun Li, Zeyu Cui, Shu Wu, Xiaoyu Zhang, and Liang Wang. 2019. Fi-gnn: Modeling feature interactions via graph neural networks for ctr prediction. In Proceedings of the 28th ACM international conference on information and knowledge management. 539–548

    2019

  25. [33]

    Jianxun Lian, Xiaohuan Zhou, Fuzheng Zhang, Zhongxia Chen, Xing Xie, and Guangzhong Sun. 2018. xdeepfm: Combining explicit and implicit feature in- teractions for recommender systems. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data ...

    2018

  26. [34]

    Bin Liu, Ruiming Tang, Yingzhi Chen, Jinkai Yu, Huifeng Guo, and Yuzhou Zhang

  27. [35]

    Qiang Liu, Feng Yu, Shu Wu, and Liang Wang. 2015. A convolutional click prediction model. In Proceedings of the 24th ACM international on conference on information and knowledge management . 1743–1746

    2015

  28. [36]

    Weiwen Liu, Wei Guo, Yong Liu, Ruiming Tang, and Hao Wang. 2023. User Behavior Modeling with Deep Learning for Recommendation: Recent Advances. In Proceedings of the 17th ACM Conference on Recommender Systems . 1286–1287

    2023

  29. [37]

    Zihan Liu, Yupeng Hou, and Julian McAuley. 2024. Multi-Behavior Generative Recommendation. arXiv preprint arXiv:2405.16871 (2024)

    2024 arXiv

  30. [38]

    Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances 9 WWW ’25, 28 April - 2 May, 2025, Sydn...

    2019

  31. [39]

    Yingzhe Peng, Xinting Hu, Jiawei Peng, Xin Geng, Xu Yang, et al. [n. d.]. LIVE: Learnable In-Context Vector for Visual Question Answering. InThe Thirty-eighth Annual Conference on Neural Information Processing Systems

  32. [40]

    Yanru Qu, Han Cai, Kan Ren, Weinan Zhang, Yong Yu, Ying Wen, and Jun Wang. 2016. Product-based Neural Networks for User Response Prediction. arXiv:1611.00144 [cs.LG] https://arxiv.org/abs/1611.00144

    2016 arXiv

  33. [41]

    Yanru Qu, Bohui Fang, Weinan Zhang, Ruiming Tang, Minzhe Niu, Huifeng Guo, Yong Yu, and Xiuqiang He. 2018. Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data. arXiv:1807.00311 [cs.IR] https://arxiv.org/abs/1807.00311

    2018 arXiv

  34. [42]

    Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research 21, 140 (2020), 1–67

    2020

  35. [43]

    Steffen Rendle. 2010. Factorization machines. In2010 IEEE International conference on data mining. IEEE, 995–1000

    2010

  36. [44]

    Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme

  37. [45]

    Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factor- izing personalized markov chains for next-basket recommendation. InProceedings of the 19th international conference on World wide web . 811–820

    2010

  38. [46]

    Noam Shazeer. 2020. Glu variants improve transformer. arXiv preprint arXiv:2002.05202 (2020)

    2020 arXiv

  39. [47]

    Tingjia Shen, Hao Wang, Chuhan Wu, Jin Yao Chin, Wei Guo, Yong Liu, Huifeng Guo, Defu Lian, Ruiming Tang, and Enhong Chen. 2024. Predictive Models in Sequential Recommendations: Bridging Performance Laws with Data Quality Insights. arXiv preprint arXiv:2412.00430 (2024)

    2024 arXiv

  40. [48]

    Tingjia Shen, Hao Wang, Jiaqing Zhang, Sirui Zhao, Liangyue Li, Zulong Chen, Defu Lian, and Enhong Chen. 2024. Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendation. arXiv preprint arXiv:2406.03085 (2024)

    2024 arXiv

  41. [49]

    Kyuyong Shin, Hanock Kwak, Su Young Kim, Max Nihlén Ramström, Jisu Jeong, Jung-Woo Ha, and Kyung-Min Kim. 2023. Scaling law for recommendation models: Towards general-purpose user representations. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 37. 4596–4604

    2023

  42. [50]

    Weiping Song, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, and Jian Tang. 2019. Autoint: Automatic feature interaction learning via self- attentive neural networks. InProceedings of the 28th ACM international conference on information and knowledge management ...

    2019

  43. [51]

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

  44. [52]

    Jiaxi Tang and Ke Wang. 2018. Personalized top-n sequential recommenda- tion via convolutional sequence embedding. In Proceedings of the eleventh ACM international conference on web search and data mining . 565–573

    2018

  45. [53]

    Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)

    2023 arXiv

  46. [54]

    A Vaswani. 2017. Attention is all you need. Advances in Neural Information Processing Systems (2017)

    2017

  47. [55]

    Hao Wang, Defu Lian, Hanghang Tong, Qi Liu, Zhenya Huang, and Enhong Chen. 2021. Hypersorec: Exploiting hyperbolic user and item representations with multiple aspects for social-aware recommendation. ACM Transactions on Information Systems (TOIS) 40, 2 (2021), 1–28

    2021

  48. [56]

    Hao Wang, Tong Xu, Qi Liu, Defu Lian, Enhong Chen, Dongfang Du, Han Wu, and Wen Su. 2019. MCNE: An end-to-end framework for learning multiple conditional network representations of social network. In Proceedings of the 25th ACM SIGKDD international conference on knowledge disc...

    2019

  49. [57]

    Hao Wang, Mingjia Yin, Luankang Zhang, Sirui Zhao, and Enhong Chen. [n. d.]. MF-GSLAE: A Multi-Factor User Representation Pre-training Framework for Dual-Target Cross-Domain Recommendation. ACM Transactions on Information Systems ([n. d.])

  50. [58]

    Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. 2017. Deep & cross network for ad click predictions. In Proceedings of the ADKDD’17 . 1–7

    2017

  51. [59]

    In Proceedings of the 28th ACM international conference on information and knowledge management

    BERT4Rec: Sequential recommendation with bidirectional encoder rep- resentations from transformer. In Proceedings of the 28th ACM international conference on information and knowledge management . 1441–1450

  52. [60]

    Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, Tingjia Shen, Chuan Qin, Chen Zhu, Hengshu Zhu, Qi Liu, et al . 2024. A survey on large language models for recommendation. World Wide Web 27, 5 (2024), 60

    2024

  53. [61]

    Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. 2019. Session-based recommendation with graph neural networks. In Proceedings of the AAAI conference on artificial intelligence , Vol. 33. 346–353

    2019

  54. [62]

    Wenjia Xie, Hao Wang, Luankang Zhang, Rui Zhou, Defu Lian, and Enhong Chen

  55. [63]

    Wenjia Xie, Rui Zhou, Hao Wang, Tingjia Shen, and Enhong Chen. 2024. Bridging User Dynamics: Transforming Sequential Recommendations with Schrödinger Bridge and Diffusion Models. In Proceedings of the 33rd ACM International Con- ference on Information and Knowledge Management ...

    2024

  56. [64]

    Xiang Xu, Hao Wang, Wei Guo, Luankang Zhang, Wanshan Yang, Runlong Yu, Yong Liu, Defu Lian, and Enhong Chen. 2024. Multi-granularity Interest Retrieval and Refinement Network for Long-Term User Behavior Modeling in CTR Prediction. arXiv preprint arXiv:2411.15005 (2024)

    2024 arXiv

  57. [65]

    Xu Yang, Yingzhe Peng, Haoxuan Ma, Shuo Xu, Chi Zhang, Yucheng Han, and Hanwang Zhang. 2023. Lever LM: configuring in-context sequence to lever large vision language models. In The Thirty-eighth Annual Conference on Neural Information Processing Systems

    2023

  58. [66]

    Xu Yang, Yongliang Wu, Mingzhuo Yang, Haokun Chen, and Xin Geng. 2024. Exploring diverse in-context configurations for image captioning. Advances in Neural Information Processing Systems 36 (2024)

    2024

  59. [67]

    Ruoxi Wang, Rakesh Shivanna, Derek Cheng, Sagar Jain, Dong Lin, Lichan Hong, and Ed Chi. 2021. Dcn v2: Improved deep & cross network and practical lessons for web-scale learning to rank systems. In Proceedings of the web conference 2021 . 1785–1797

    2021

  60. [68]

    Mingjia Yin, Hao Wang, Wei Guo, Yong Liu, Suojuan Zhang, Sirui Zhao, Defu Lian, and Enhong Chen. 2024. Dataset regeneration for sequential recommendation. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 3954–3965

    2024

  61. [69]

    Mingjia Yin, Hao Wang, Xiang Xu, Likang Wu, Sirui Zhao, Wei Guo, Yong Liu, Ruiming Tang, Defu Lian, and Enhong Chen. 2023. APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation. In Proceedings of the 32nd ACM ...

    2023

  62. [70]

    Mingjia Yin, Chuhan Wu, Yufei Wang, Hao Wang, Wei Guo, Yasheng Wang, Yong Liu, Ruiming Tang, Defu Lian, and Enhong Chen. 2024. Entropy law: The story behind data compression and llm performance. arXiv preprint arXiv:2407.06645 (2024)

    2024 arXiv

  63. [71]

    arXiv preprint arXiv:2410.23994 (2024)

    Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model. arXiv preprint arXiv:2410.23994 (2024)

    2024 arXiv

  64. [72]

    Jiaqi Zhai, Lucy Liao, Xing Liu, Yueming Wang, Rui Li, Xuan Cao, Leon Gao, Zhao- jie Gong, Fangda Gu, Michael He, et al. 2024. Actions speak louder than words: Trillion-parameter sequential transducers for generative recommendations.arXiv preprint arXiv:2402.17152 (2024)

    2024 arXiv

  65. [73]

    Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer. 2022. Scaling Vision Transformers. arXiv:2106.04560 [cs.CV] https://arxiv.org/abs/2106.04560

    2022 arXiv

  66. [74]

    Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer. 2022. Scaling vision transformers. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 12104–12113

    2022

  67. [75]

    Biao Zhang and Rico Sennrich. 2019. Root Mean Square Layer Normaliza- tion. In Advances in Neural Information Processing Systems 32: Annual Con- ference on Neural Information Processing Systems 2019, NeurIPS 2019, Decem- ber 8-14, 2019, Vancouver, BC, Canada , Hanna M. Wallach...

    2019

  68. [76]

    Mingjia Yin, Hao Wang, Wei Guo, Yong Liu, Zhi Li, Sirui Zhao, Zhen Wang, Defu Lian, and Enhong Chen. 2024. Learning Partially Aligned Item Representation for Cross-Domain Sequential Recommendation. arXiv preprint arXiv:2405.12473 (2024)

    2024 arXiv

  69. [77]

    Luankang Zhang, Hao Wang, Suojuan Zhang, Mingjia Yin, Yongqiang Han, Jiaqing Zhang, Defu Lian, and Enhong Chen. 2024. A Unified Framework for Adaptive Representation Enhancement and Inversed Learning in Cross-Domain Recommendation. arXiv preprint arXiv:2404.00268 (2024)

    2024 arXiv

  70. [78]

    Weinan Zhang, Tianming Du, and Jun Wang. 2016. Deep Learning over Multi- field Categorical Data: –A Case Study on User Response Prediction. In Advances in Information Retrieval: 38th European Conference on IR Research, ECIR 2016, Padua, Italy, March 20–23, 2016. Proceedings 38...

    2016

  71. [79]

    Xikun Zhang, Dongjin Song, Yushan Jiang, Yixin Chen, and Dacheng Tao. 2024. Learning System Dynamics without Forgetting. arXiv preprint arXiv:2407.00717 (2024)

    2024 arXiv

  72. [81]

    Xikun Zhang, Dongjin Song, and Dacheng Tao. 2022. Hierarchical prototype networks for continual graph representation learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 4 (2022), 4622–4636. 10 FuXi-𝛼: Scaling Recommendation Model with Feature Interacti...

    2022

  73. [82]

    Xikun Zhang, Chang Xu, and Dacheng Tao. 2020. Context aware graph con- volution for skeleton-based action recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 14333–14342

    2020

  74. [83]

    Xikun Zhang, Chang Xu, Xinmei Tian, and Dacheng Tao. 2019. Graph edge convo- lutional neural networks for skeleton-based action recognition. IEEE transactions on neural networks and learning systems 31, 8 (2019), 3047–3060

    2019

  75. [84]

    Yuren Zhang, Enhong Chen, Binbin Jin, Hao Wang, Min Hou, Wei Huang, and Runlong Yu. 2022. 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 R...

    2022

  76. [85]

    Gaowei Zhang, Yupeng Hou, Hongyu Lu, Yu Chen, Wayne Xin Zhao, and Ji-Rong Wen. 2023. Scaling Law of Large Sequential Recommendation Models. arXiv preprint arXiv:2311.11351 (2023)

    2023 arXiv

  77. [89]

    Xikun Zhang, Dongjin Song, and Dacheng Tao. 2022. Cglb: Benchmark tasks for continual graph learning. Advances in Neural Information Processing Systems 35 (2022), 13006–13021

    2022

  78. [2012]

    arXiv preprint arXiv:1205.2618 (2012)

    BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012)

    2012 arXiv

  79. [2016]

    arXiv:1606.07792 [cs.LG] https://arxiv.org/abs/1606.07792

    Wide & Deep Learning for Recommender Systems. arXiv:1606.07792 [cs.LG] https://arxiv.org/abs/1606.07792

    arXiv

  80. [2017]

    In International conference on machine learning

    Convolutional sequence to sequence learning. In International conference on machine learning. PMLR, 1243–1252

  81. [2018]

    In The 41st international ACM SIGIR conference on research & development in information retrieval

    Improving sequential recommendation with knowledge-enhanced mem- ory networks. In The 41st international ACM SIGIR conference on research & development in information retrieval. 505–514

  82. [2019]

    In The World Wide Web Conference

    Feature generation by convolutional neural network for click-through rate prediction. In The World Wide Web Conference. 1119–1129

  83. [2020]

    arXiv preprint arXiv:2010.14701 (2020)

    Scaling laws for autoregressive generative modeling. arXiv preprint arXiv:2010.14701 (2020)

    2020 arXiv

  84. [2024]

    arXiv:2407.13349 [cs.IR] https://arxiv.org/abs/2407.13349

    DCNv3: Towards Next Generation Deep Cross Network for CTR Prediction. arXiv:2407.13349 [cs.IR] https://arxiv.org/abs/2407.13349

Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.