research-article

Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation

Authors:

Enhong ChenAuthors Info & Claims

WWW '24: Proceedings of the ACM Web Conference 2024

Pages 3297 - 3306

https://doi.org/10.1145/3589334.3645380

Published: 13 May 2024 Publication History

Abstract

In recommendation systems, users frequently engage in multiple types of behaviors, such as clicking, adding to cart, and purchasing. Multi-behavior sequential recommendation aims to jointly consider multiple behaviors to improve the target behavior's performance. However, with diversified behavior data, user behavior sequences will become very long in the short term, which brings challenges to the efficiency of the sequence recommendation model. Meanwhile, some behavior data will also bring inevitable noise to the modeling of user interests. To address the aforementioned issues, firstly, we develop the Efficient Behavior Sequence Miner (EBM) that efficiently captures intricate patterns in user behavior while maintaining low time complexity and parameter count. Secondly, we design hard and soft denoising modules for different noise types and fully explore the relationship between behaviors and noise. Finally, we introduce a contrastive loss function along with a guided training strategy to contrast the valid information with the noisy signal in the data, and seamlessly integrate the two denoising processes to achieve a high degree of decoupling of the noisy signal. Sufficient experiments on real-world datasets demonstrate the effectiveness and efficiency of our approach in dealing with multi-behavior sequential recommendation.

Supplemental Material

MP4 File

Supplemental video

Download
24.83 MB

References

[1]

Kwan Ho Ryan Chan et al. "ReduNet: A white-box deep network from the principle of maximizing rate reduction". In: The Journal of Machine Learning Research 23.1 (2022), pp. 4907--5009.

[2]

Chong Chen et al. "Efficient heterogeneous collaborative filtering without negative sampling for recommendation". In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 34. 01. 2020, pp. 19--26.

[3]

Chong Chen et al. "Efficient heterogeneous collaborative filtering without negative sampling for recommendation". In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 34. 01. 2020, pp. 19--26.

[4]

Hong Chen et al. "Curriculum disentangled recommendation with noisy multifeedback". In: Advances in Neural Information Processing Systems 34 (2021), pp. 26924--26936.

[5]

Lu Chi, Borui Jiang, and Yadong Mu. "Fast fourier convolution". In: Advances in Neural Information Processing Systems 33 (2020), pp. 4479--4488.

[6]

Prem C Consul and Gaurav C Jain. "A generalization of the Poisson distribution". In: Technometrics 15.4 (1973), pp. 791--799.

[7]

Hanwen Du et al. "Contrastive Learning with Bidirectional Transformers for Sequential Recommendation". In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2022, pp. 396--405.

[8]

Xinyu Du et al. "Frequency enhanced hybrid attention network for sequential recommendation". In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2023, pp. 78-- 88.

[9]

Chen Gao et al. "Neural multi-task recommendation from multi-behavior data". In: 2019 IEEE 35th international conference on data engineering (ICDE). IEEE. 2019, pp. 1554--1557.

[10]

Prem Gopalan, Jake M Hofman, and David M Blei. "Scalable Recommendation with Hierarchical Poisson Factorization." In: UAI. 2015, pp. 326--335.

Digital Library

[11]

Shuyun Gu et al. "Self-supervised Graph Neural Networks for Multi-behavior Recommendation". In: International Joint Conference on Artificial Intelligence (IJCAI). 2022.

[12]

Long Guo et al. "Buying or browsing?: Predicting real-time purchasing intent using attention-based deep network with multiple behavior". In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019, pp. 1984--1992.

[13]

Yongqiang Han et al. "GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation". In: Database Systems for Advanced Applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17--20, 2023, Proceedings, Part II. Springer. 2023, pp. 286-- 296.

[14]

Seyed Abbas Hosseini et al. "Recurrent poisson factorization for temporal recommendation". In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017, pp. 847--855.

[15]

Bowen Jin et al. "Multi-behavior recommendation with graph convolutional networks". In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020, pp. 659--668.

[16]

Wang-Cheng Kang and Julian McAuley. "Self-attentive sequential recommendation". In: 2018 IEEE international conference on data mining (ICDM). IEEE. 2018, pp. 197--206.

[17]

Youngho Kim et al. "Modeling dwell time to predict click-level satisfaction". In: Proceedings of the 7th ACM international conference on Web search and data mining. 2014, pp. 193--202.

[18]

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

Digital Library

[19]

MindSpore. 2020. url: https://www.mindspore.cn.

[20]

Henri J Nussbaumer and Henri J Nussbaumer. The fast Fourier transform. Springer, 1982.

[21]

Hongbin Pei et al. "Active Surveillance via Group Sparse Bayesian Learning". In: IEEE Transactions on Pattern Analysis and Machine Intelligence 44.3 (2022), pp. 1133--1148.

[22]

Hongbin Pei et al. "Geom-GCN: Geometric Graph Convolutional Networks". In: International Conference on Learning Representations. 2020.

[23]

Yuqi Qin, Pengfei Wang, and Chenliang Li. "The world is binary: Contrastive learning for denoising next basket recommendation". In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval. 2021, pp. 859--868.

Digital Library

[24]

Fei Sun et al. "BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer". In: Proceedings of the 28th ACM international conference on information and knowledge management. 2019, pp. 1441-- 1450.

[25]

Yatong Sun et al. "Does Every Data Instance Matter? Enhancing Sequential Recommendation by Eliminating Unreliable Data." In: IJCAI. 2021, pp. 1579-- 1585.

[26]

Xiaohai Tong et al. "Pattern-enhanced Contrastive Policy Learning Network for Sequential Recommendation." In: IJCAI. 2021, pp. 1593--1599.

[27]

Hao Wang et al. ?Hypersorec: Exploiting hyperbolic user and item representations with multiple aspects for social-aware recommendation". In: ACM Transactions on Information Systems (TOIS) 40.2 (2021), pp. 1--28.

Digital Library

[28]

Hao Wang et al. "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 discovery & data mining. 2019, pp. 1064--1072.

[29]

Mengzhu Wang et al. "Inter-Class and Inter-Domain Semantic Augmentation for Domain Generalization". In: IEEE Transactions on Image Processing (2024).

[30]

Qinyong Wang et al. "Enhancing collaborative filtering with generative augmentation". In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019, pp. 548--556.

[31]

Wenjie Wang et al. "Denoising implicit feedback for recommendation". In: Proceedings of the 14th ACM international conference on web search and data mining. 2021, pp. 373--381.

[32]

Wei Wei et al. "Contrastive meta learning with behavior multiplicity for recommendation". In: Proceedings of the fifteenth ACM international conference on web search and data mining. 2022, pp. 1120--1128.

Digital Library

[33]

Tzu-Tsung Wong. "Performance evaluation of classification algorithms by kfold and leave-one-out cross validation". In: Pattern recognition 48.9 (2015), pp. 2839--2846.

Digital Library

[34]

Chuhan Wu et al. "Feedrec: News feed recommendation with various user feedbacks". In: Proceedings of the ACM Web Conference 2022. 2022, pp. 2088-- 2097.

[35]

Likang Wu et al. "A Survey on Large Language Models for Recommendation". In: arXiv preprint arXiv:2305.19860 (2023).

[36]

Yiqing Wu et al. "Multi-view multi-behavior contrastive learning in recommendation". In: Database Systems for Advanced Applications: 27th International Conference, DASFAA 2022, Virtual Event, April 11--14, 2022, Proceedings, Part II. Springer. 2022, pp. 166--182.

[37]

Yiqing Wu et al. "Personalized Prompts for Sequential Recommendation". In: arXiv preprint arXiv:2205.09666 (2022).

[38]

Lianghao Xia et al. "Graph meta network for multi-behavior recommendation". In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval. 2021, pp. 757--766.

[39]

Lianghao Xia et al. "Graph meta network for multi-behavior recommendation". In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval. 2021, pp. 757--766.

[40]

Lianghao Xia et al. "Knowledge-enhanced hierarchical graph transformer network for multi-behavior recommendation". In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. 5. 2021, pp. 4486--4493.

[41]

Lianghao Xia et al. "Multi-Behavior Sequential Recommendation with Temporal Graph Transformer". In: IEEE Transactions on Knowledge and Data Engineering (2022).

[42]

Lianghao Xia et al. "Multiplex behavioral relation learning for recommendation via memory augmented transformer network". In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval. 2020, pp. 2397--2406.

[43]

Xu Xie et al. "Contrastive learning for sequential recommendation". In: 2022 IEEE 38th international conference on data engineering (ICDE). IEEE. 2022, pp. 1259--1273.

[44]

Hongrui Xuan et al. "Knowledge Enhancement for Contrastive Multi-Behavior Recommendation". In: Proceedings of the Sixteenth ACMInternational Conference on Web Search and Data Mining. 2023, pp. 195--203.

[45]

Yuhao Yang et al. "Multi-behavior hypergraph-enhanced transformer for sequential recommendation". In: Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining. 2022, pp. 2263--2274.

[46]

Yuhao Yang et al. "Multi-behavior hypergraph-enhanced transformer for sequential recommendation". In: Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining. 2022, pp. 2263--2274.

[47]

Mingjia Yin et al. "APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation". In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 2023, pp. 3009--3019.

[48]

Junliang Yu et al. "Generating reliable friends via adversarial training to improve social recommendation". In: 2019 IEEE international conference on data mining (ICDM). IEEE. 2019, pp. 768--777.

[49]

Enming Yuan et al. "Multi-behavior sequential transformer recommender". In: Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval. 2022, pp. 1642--1652.

[50]

Jiahao Yuan et al. "Dual sparse attention network for session-based recommendation". In: Proceedings of the AAAI conference on artificial intelligence. Vol. 35. 5. 2021, pp. 4635--4643.

[51]

Chi Zhang et al. "Denoising and Prompt-Tuning for Multi-Behavior Recommendation". In: Proceedings of the ACM Web Conference 2023. 2023, pp. 1355-- 1363.

[52]

Lei Zhang et al. "SHGCN: Socially enhanced heterogeneous graph convolutional network for multi-behavior prediction". In: ACM Transactions on the Web 18.1 (2023), pp. 1--27.

Digital Library

[53]

Qian Zhao et al. "Gaze prediction for recommender systems". In: Proceedings of the 10th ACM Conference on Recommender Systems. 2016, pp. 131--138.

[54]

Zhe Zhao et al. "Improving user topic interest profiles by behavior factorization". In: Proceedings of the 24th International Conference on World Wide Web. 2015, pp. 1406--1416.

Digital Library

[55]

Guorui Zhou et al. "Deep interest network for click-through rate prediction". In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 2018, pp. 1059--1068.

[56]

Kun Zhou et al. "Filter-enhanced MLP is all you need for sequential recommendation". In: Proceedings of the ACM web conference 2022. 2022, pp. 2388-- 2399.

Cited By

Zhang YChen WCai FShi ZQi F(2025)DMR: disentangled and denoised learning for multi-behavior recommendationComplex & Intelligent Systems10.1007/s40747-024-01778-511:2Online publication date: 16-Jan-2025
https://doi.org/10.1007/s40747-024-01778-5
Zhang LWang HZhang SYin MHan YZhang JLian DChen E(2025)A Unified Framework for Adaptive Representation Enhancement and Inversed Learning in Cross-Domain RecommendationDatabase Systems for Advanced Applications10.1007/978-981-97-5555-4_8(115-130)Online publication date: 12-Jan-2025
https://doi.org/10.1007/978-981-97-5555-4_8
Xie WZhou RWang HShen TChen ESerra ESpezzano F(2024)Bridging User Dynamics: Transforming Sequential Recommendations with Schrödinger Bridge and Diffusion ModelsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679756(2618-2628)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679756
Show More Cited By

Index Terms

Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation
1. Information systems
  1. Information retrieval
    1. Users and interactive retrieval
      1. Personalization

Recommendations

Multi-interest sequential recommendation with contrastive learning and temporal analysis
Abstract
Sequential recommendation systems aim to forecast the subsequent item of interest to users by analyzing their historical behaviors. While existing approaches, which employ attention mechanisms, have significantly advanced by capturing users’ ...
Highlights
- Proposing a multi-interest recommendation approach that incorporates user preference features and temporal characteristics at the user level.
- Introducing a time-period module to capture short-term changes in user interest.
- ...
Reducing Interaction Noise for Sequential Recommendation via Robust Interests
Database Systems for Advanced Applications
Abstract
Contrastive learning can assist sequential recommendation in improving the performance of predicting the next-interacted item, often by mining self-supervised signals in augmented sequences. However, the noise in the original interaction sequences ...
Personalized Behavior-Aware Transformer for Multi-Behavior Sequential Recommendation
MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Sequential Recommendation (SR) captures users' dynamic preferences by modeling how users transit among items. However, SR models that utilize only single type of behavior interaction data encounter performance degradation when the sequences are short. To ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

WWW '24: Proceedings of the ACM Web Conference 2024

May 2024

4826 pages

ISBN:9798400701719

DOI:10.1145/3589334

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Chong-Wah Ngo
Singapore Management University
,
Proceedings Chair:
Roy Ka-Wei Lee
Singapore University of Technology and Design
,
Program Chairs:
Ravi Kumar
Google
,
Hady W. Lauw
Singapore Management University

Copyright © 2024 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2024

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

WWW '24

Sponsor:

SIGWEB

WWW '24: The ACM Web Conference 2024

May 13 - 17, 2024

Singapore, Singapore

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
496
Total Downloads

Downloads (Last 12 months)496
Downloads (Last 6 weeks)45

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zhang YChen WCai FShi ZQi F(2025)DMR: disentangled and denoised learning for multi-behavior recommendationComplex & Intelligent Systems10.1007/s40747-024-01778-511:2Online publication date: 16-Jan-2025
https://doi.org/10.1007/s40747-024-01778-5
Zhang LWang HZhang SYin MHan YZhang JLian DChen E(2025)A Unified Framework for Adaptive Representation Enhancement and Inversed Learning in Cross-Domain RecommendationDatabase Systems for Advanced Applications10.1007/978-981-97-5555-4_8(115-130)Online publication date: 12-Jan-2025
https://doi.org/10.1007/978-981-97-5555-4_8
Xie WZhou RWang HShen TChen ESerra ESpezzano F(2024)Bridging User Dynamics: Transforming Sequential Recommendations with Schrödinger Bridge and Diffusion ModelsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679756(2618-2628)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679756
Tong JYin MWang HPan QLian Dchen E(2024)MDAP: A Multi-view Disentangled and Adaptive Preference Learning Framework for Cross-Domain RecommendationWeb Information Systems Engineering – WISE 202410.1007/978-981-96-0570-5_12(164-178)Online publication date: 30-Nov-2024
https://doi.org/10.1007/978-981-96-0570-5_12

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten