Yongqiang Han
Enhong Chen
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
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- Hong Chen et al. "Curriculum disentangled recommendation with noisy multifeedback". In: Advances in Neural Information Processing Systems 34 (2021), pp. 26924--26936.Google Scholar
- Lu Chi, Borui Jiang, and Yadong Mu. "Fast fourier convolution". In: Advances in Neural Information Processing Systems 33 (2020), pp. 4479--4488.Google Scholar
- Prem C Consul and Gaurav C Jain. "A generalization of the Poisson distribution". In: Technometrics 15.4 (1973), pp. 791--799.Google ScholarCross Ref
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- Prem Gopalan, Jake M Hofman, and David M Blei. "Scalable Recommendation with Hierarchical Poisson Factorization." In: UAI. 2015, pp. 326--335.Google ScholarDigital Library
- Shuyun Gu et al. "Self-supervised Graph Neural Networks for Multi-behavior Recommendation". In: International Joint Conference on Artificial Intelligence (IJCAI). 2022.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- Wang-Cheng Kang and Julian McAuley. "Self-attentive sequential recommendation". In: 2018 IEEE international conference on data mining (ICDM). IEEE. 2018, pp. 197--206.Google ScholarCross Ref
- 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.Google Scholar
- 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.Google ScholarDigital Library
- MindSpore. 2020. url: https://www.mindspore.cn.Google Scholar
- Henri J Nussbaumer and Henri J Nussbaumer. The fast Fourier transform. Springer, 1982.Google ScholarCross Ref
- 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. doi: 10.1109/TPAMI.2020.3023092.Google ScholarCross Ref
- Hongbin Pei et al. "Geom-GCN: Geometric Graph Convolutional Networks". In: International Conference on Learning Representations. 2020.Google Scholar
- 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.Google ScholarDigital Library
- 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.Google Scholar
- Yatong Sun et al. "Does Every Data Instance Matter? Enhancing Sequential Recommendation by Eliminating Unreliable Data." In: IJCAI. 2021, pp. 1579-- 1585.Google Scholar
- Xiaohai Tong et al. "Pattern-enhanced Contrastive Policy Learning Network for Sequential Recommendation." In: IJCAI. 2021, pp. 1593--1599.Google Scholar
- 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.Google ScholarDigital Library
- 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.Google Scholar
- Mengzhu Wang et al. "Inter-Class and Inter-Domain Semantic Augmentation for Domain Generalization". In: IEEE Transactions on Image Processing (2024).Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- Chuhan Wu et al. "Feedrec: News feed recommendation with various user feedbacks". In: Proceedings of the ACM Web Conference 2022. 2022, pp. 2088-- 2097.Google Scholar
- Likang Wu et al. "A Survey on Large Language Models for Recommendation". In: arXiv preprint arXiv:2305.19860 (2023).Google Scholar
- 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.Google Scholar
- Yiqing Wu et al. "Personalized Prompts for Sequential Recommendation". In: arXiv preprint arXiv:2205.09666 (2022).Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- Lianghao Xia et al. "Multi-Behavior Sequential Recommendation with Temporal Graph Transformer". In: IEEE Transactions on Knowledge and Data Engineering (2022).Google Scholar
- 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.Google Scholar
- Xu Xie et al. "Contrastive learning for sequential recommendation". In: 2022 IEEE 38th international conference on data engineering (ICDE). IEEE. 2022, pp. 1259--1273.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- Chi Zhang et al. "Denoising and Prompt-Tuning for Multi-Behavior Recommendation". In: Proceedings of the ACM Web Conference 2023. 2023, pp. 1355-- 1363.Google Scholar
- 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.Google ScholarDigital Library
- Qian Zhao et al. "Gaze prediction for recommender systems". In: Proceedings of the 10th ACM Conference on Recommender Systems. 2016, pp. 131--138.Google Scholar
- 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.Google ScholarDigital Library
- 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.Google Scholar
- 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.Google Scholar
Index Terms
- Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation
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