The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
Filter-enhanced MLP is All You Need for Sequential Recommendation
Open accessAbstract
Recently, deep neural networks such as RNN, CNN and Transformer have been applied in the task of sequential recommendation, which aims to capture the dynamic preference characteristics from logged user behavior data for accurate recommendation. However, in online platforms, logged user behavior data is inevitable to contain noise, and deep recommendation models are easy to overfit on these logged data. To tackle this problem, we borrow the idea of filtering algorithms from signal processing that attenuates the noise in the frequency domain. In our empirical experiments, we find that filtering algorithms can substantially improve representative sequential recommendation models, and integrating simple filtering algorithms (e.g., Band-Stop Filter) with an all-MLP architecture can even outperform competitive Transformer-based models. Motivated by it, we propose FMLP-Rec, an all-MLP model with learnable filters for sequential recommendation task. The all-MLP architecture endows our model with lower time complexity, and the learnable filters can adaptively attenuate the noise information in the frequency domain. Extensive experiments conducted on eight real-world datasets demonstrate the superiority of our proposed method over competitive RNN, CNN, GNN and Transformer-based methods. Our code and data are publicly available at the link: https://github.com/RUCAIBox/FMLP-Rec .
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
Jakubův, J., Chvalovský, K., Goertzel, Z., Kaliszyk, C., Olšák, M., Piotrowski, B., Schulz, S., Suda, M., & Urban, J. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,658
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
(2014) · Cited by 33,419
The Design and Implementation of FFTW3
Frigo, M., & Johnson, S. G. (2005) · Proceedings of the IEEE · doi:10.1109/jproc.2004.840301 · Cited by 5,086
Rendle, S. (2010) · 2010 IEEE International Conference on Data Mining · doi:10.1109/icdm.2010.127 · Cited by 3,189
Self-Attentive Sequential Recommendation
Kang, W.-C., & McAuley, J. (2018) · 2018 IEEE International Conference on Data Mining (ICDM) · doi:10.1109/icdm.2018.00035 · Cited by 3,011
Theory and Application of Digital Signal Processing
Rabiner, L. R., Gold, B., & Yuen, C. K. (1978) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1978.4309918 · Cited by 2,797
Factorizing personalized Markov chains for next-basket recommendation
Rendle, S., Freudenthaler, C., & Schmidt-Thieme, L. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772773 · Cited by 2,692
Cited by
Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders
Hou, Y., He, Z., McAuley, J., & Zhao, W. X. (2023) · Proceedings of the ACM Web Conference 2023 · doi:10.1145/3543507.3583434 · Cited by 172
“Early works follow the Markov Chain assumption [39], while recent studies mainly focus on designing diferent neural network models, including Recurrent Neural Network (RNN) [16, 30], Convolutional Neural Network (CNN) [45], Transformer [15, 17, 24, 44], Graph Neural Network (GNN) [3, 54] and Multilayer Perceptron (MLP…”
BackgroundSelf-citationAdapting Large Language Models by Integrating Collaborative Semantics for Recommendation
Zheng, B., Hou, Y., Lu, H., Chen, Y., Zhao, W. X., Chen, M., & Wen, J.-R. (2024) · 2024 IEEE 40th International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00118 · Cited by 143
Self-citationShilling Black-Box Recommender Systems by Learning to Generate Fake User Profiles
Lin, C., Chen, S., Zeng, M., Zhang, S., Gao, M., & Li, H. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3183210 · Cited by 62
Metadata
- Authors
- Zhou, K., Yu, H., Zhao, W. X., & Wen, J.-R.
- Venue
- Proceedings of the ACM Web Conference 2022
- Publisher
- ACM
- Published
- 2022-04-25
- Pages
- 2388-2399
- OpenAlex
- W4220974940
- Cited by
- 401
- References
- 64
- FWCI
- 68.89
- Type
- conference paper
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Filter-enhanced MLP is All You Need for Sequential Recommendation. https://app.cochat.ai/papers/10.1145/3485447.3512111
- The paper
- Zhou, K., Yu, H., Zhao, W. X., & Wen, J.-R. (2022). Filter-enhanced MLP is All You Need for Sequential Recommendation. Proceedings of the ACM Web Conference 2022. https://doi.org/10.1145/3485447.3512111
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
