research-article

Global Heterogeneous Graph and Target Interest Denoising for Multi-behavior Sequential Recommendation

Authors:

Mei YuAuthors Info & Claims

WSDM '24: Proceedings of the 17th ACM International Conference on Web Search and Data Mining

Pages 387 - 395

https://doi.org/10.1145/3616855.3635857

Published: 04 March 2024 Publication History

Abstract

Multi-behavior sequential recommendation (MBSR) predicts a user's next item of interest based on their interaction history across different behavior types. Although existing studies have proposed capturing the correlation between different types of behavior, two important challenges have not been explored: i) Dealing with heterogeneous item transitions (both global and local perspectives). ii) Mitigating the issue of noise that arises from the incorporation of auxiliary behaviors. To address these issues, we propose a novel solution, Global Heterogeneous Graph and Target Interest Denoising for Multi-behavior Sequential Recommendation (GHTID). In particular, we view the transitions between behavior types of items as different relationships and propose two heterogeneous graphs. By considering the relationship between items under different behavioral types of transformations, we propose two heterogeneous graph convolution modules and explicitly learn heterogeneous item transitions. Moreover, we utilize two attention networks to integrate long-term and short-term interests associated with the target behavior to alleviate the noisy interference of auxiliary behaviors. Extensive experiments on four real-world datasets demonstrate that our method outperforms other state-of-the-art methods.

References

[1]

Chong Chen, Min Zhang, Yongfeng Zhang, Weizhi Ma, Yiqun Liu, and Shaoping Ma. 2020. Efficient heterogeneous collaborative filtering without negative sampling for recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 19--26.

[2]

Weixin Chen, Mingkai He, Yongxin Ni, Weike Pan, Li Chen, and Zhong Ming. 2022. Global and Personalized Graphs for Heterogeneous Sequential Recommendation by Learning Behavior Transitions and User Intentions. In Proceedings of the 16th ACM Conference on Recommender Systems. 268--277.

Digital Library

[3]

Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017. Neural message passing for quantum chemistry. In International conference on machine learning. PMLR, 1263--1272.

[4]

Yulong Gu, Zhuoye Ding, Shuaiqiang Wang, Lixin Zou, Yiding Liu, and Dawei Yin. 2020. Deep multifaceted transformers for multi-objective ranking in large-scale e-commerce recommender systems. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2493--2500.

Digital Library

[5]

Ruining He, Wang-Cheng Kang, and Julian McAuley. 2017. Translation-based recommendation. In Proceedings of the eleventh ACM conference on recommender systems. 161--169.

Digital Library

[6]

Ruining He and Julian McAuley. 2016. Fusing similarity models with markov chains for sparse sequential recommendation. In 2016 IEEE 16th international conference on data mining (ICDM). IEEE, 191--200.

[7]

Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015).

[8]

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

[9]

Walid Krichene and Steffen Rendle. 2020. On sampled metrics for item recommendation. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. 1748--1757.

Digital Library

[10]

Dong Li, Ruoming Jin, Jing Gao, and Zhi Liu. 2020a. On sampling top-k recommendation evaluation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2114--2124.

Digital Library

[11]

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.

Digital Library

[12]

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

Digital Library

[13]

Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. 2015. Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493 (2015).

[14]

Zhi Li, Hongke Zhao, Qi Liu, Zhenya Huang, Tao Mei, and Enhong Chen. 2018. Learning from history and present: Next-item recommendation via discriminatively exploiting user behaviors. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 1734--1743.

Digital Library

[15]

Zhiwei Liu, Yongjun Chen, Jia Li, Philip S Yu, Julian McAuley, and Caiming Xiong. 2021. Contrastive self-supervised sequential recommendation with robust augmentation. arXiv preprint arXiv:2108.06479 (2021).

[16]

Jinwei Luo, Mingkai He, Xiaolin Lin, Weike Pan, and Zhong Ming. 2022. Dual-Task Learning for Multi-Behavior Sequential Recommendation. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 1379--1388.

Digital Library

[17]

Wenjing Meng, Deqing Yang, and Yanghua Xiao. 2020. Incorporating user micro-behaviors and item knowledge into multi-task learning for session-based recommendation. In Proceedings of the 43rd international ACM SIGIR conference on research and development in Information Retrieval. 1091--1100.

Digital Library

[18]

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

Digital Library

[19]

Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15, 1 (2014), 1929--1958.

Digital Library

[20]

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

Digital Library

[21]

Md Mehrab Tanjim, Congzhe Su, Ethan Benjamin, Diane Hu, Liangjie Hong, and Julian McAuley. 2020. Attentive sequential models of latent intent for next item recommendation. In Proceedings of The Web Conference 2020. 2528--2534.

Digital Library

[22]

Yu Tian, Jianxin Chang, Yanan Niu, Yang Song, and Chenliang Li. 2022. When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1632--1641.

Digital Library

[23]

Wen Wang, Wei Zhang, Shukai Liu, Qi Liu, Bo Zhang, Leyu Lin, and Hongyuan Zha. 2020b. Beyond clicks: Modeling multi-relational item graph for session-based target behavior prediction. In Proceedings of the web conference 2020. 3056--3062.

Digital Library

[24]

Ziyang Wang, Wei Wei, Gao Cong, Xiao-Li Li, Xian-Ling Mao, and Minghui Qiu. 2020a. Global context enhanced graph neural networks for session-based recommendation. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval. 169--178.

Digital Library

[25]

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.

Digital Library

[26]

Lianghao Xia, Chao Huang, Yong Xu, and Jian Pei. 2022. Multi-Behavior Sequential Recommendation with Temporal Graph Transformer. IEEE Transactions on Knowledge and Data Engineering (2022).

Digital Library

[27]

Xu Xie, Fei Sun, Zhaoyang Liu, Shiwen Wu, Jinyang Gao, Jiandong Zhang, Bolin Ding, and Bin Cui. 2022a. Contrastive learning for sequential recommendation. In 2022 IEEE 38th international conference on data engineering (ICDE). IEEE, 1259--1273.

[28]

Yueqi Xie, Peilin Zhou, and Sunghun Kim. 2022b. Decoupled side information fusion for sequential recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1611--1621.

Digital Library

[29]

Yuhao Yang, Chao Huang, Lianghao Xia, Yuxuan Liang, Yanwei Yu, and Chenliang Li. 2022. Multi-behavior hypergraph-enhanced transformer for sequential recommendation. In Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining. 2263--2274.

Digital Library

[30]

Liang Yao, Chengsheng Mao, and Yuan Luo. 2019. Graph convolutional networks for text classification. In Proceedings of the AAAI conference on artificial intelligence, Vol. 33. 7370--7377.

Digital Library

[31]

Enming Yuan, Wei Guo, Zhicheng He, Huifeng Guo, Chengkai Liu, and Ruiming Tang. 2022. Multi-behavior sequential transformer recommender. In Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval. 1642--1652.

Digital Library

[32]

Zhuoxin Zhan, Mingkai He, Weike Pan, and Zhong Ming. 2022. TransRec: Translation-based sequential recommendation with heterogeneous feedback. Frontiers Comput. Sci. 16, 2 (2022), 162615.

Digital Library

[33]

Meizi Zhou, Zhuoye Ding, Jiliang Tang, and Dawei Yin. 2018. Micro behaviors: A new perspective in e-commerce recommender systems. In Proceedings of the eleventh ACM international conference on web search and data mining. 727--735. io

Digital Library

Cited By

Duan SOuyang MWang RLi QXiao Y(2025)Let long-term interests talk: An disentangled learning model for recommendation based on short-term interests generationInformation Processing & Management10.1016/j.ipm.2024.10399762:2(103997)Online publication date: Mar-2025
https://doi.org/10.1016/j.ipm.2024.103997
Li WLin XPan WMing Z(2024)Dynamic Stage-aware User Interest Learning for Heterogeneous Sequential RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688103(465-474)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688103
Bing RYuan GZhang YZhou YYan Q(2024)Automated message selection for robust Heterogeneous Graph Contrastive LearningKnowledge-Based Systems10.1016/j.knosys.2024.112739(112739)Online publication date: Nov-2024
https://doi.org/10.1016/j.knosys.2024.112739
Show More Cited By

Index Terms

Global Heterogeneous Graph and Target Interest Denoising for Multi-behavior Sequential Recommendation
1. Information systems
  1. Information systems applications

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cover image ACM Conferences

WSDM '24: Proceedings of the 17th ACM International Conference on Web Search and Data Mining

March 2024

1246 pages

ISBN:9798400703713

DOI:10.1145/3616855

General Chairs:
Luz Angélica
Caudillo Mata (MDA Geointelligence)
,
Silvio Lattanzi
Google Research
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Andrés Muñoz Medina
Google Research
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Program Chairs:
Leman Akoglu
CMU
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Aristides Gionis
KTH
,
Sergei Vassilvitskii
Google Research

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Published: 04 March 2024

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March 4 - 8, 2024

Merida, Mexico

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Cited By

Duan SOuyang MWang RLi QXiao Y(2025)Let long-term interests talk: An disentangled learning model for recommendation based on short-term interests generationInformation Processing & Management10.1016/j.ipm.2024.10399762:2(103997)Online publication date: Mar-2025
https://doi.org/10.1016/j.ipm.2024.103997
Li WLin XPan WMing Z(2024)Dynamic Stage-aware User Interest Learning for Heterogeneous Sequential RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688103(465-474)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688103
Bing RYuan GZhang YZhou YYan Q(2024)Automated message selection for robust Heterogeneous Graph Contrastive LearningKnowledge-Based Systems10.1016/j.knosys.2024.112739(112739)Online publication date: Nov-2024
https://doi.org/10.1016/j.knosys.2024.112739
Saito YSugiyama K(2024)Leveraging multiple behaviors and explicit preferences for job recommendationExpert Systems with Applications10.1016/j.eswa.2024.125149258(125149)Online publication date: Dec-2024
https://doi.org/10.1016/j.eswa.2024.125149
Li QMa HJin WJi YLi Z(2024)Hypergraph-enhanced multi-interest learning for multi-behavior sequential recommendationExpert Systems with Applications10.1016/j.eswa.2024.124497255(124497)Online publication date: Dec-2024
https://doi.org/10.1016/j.eswa.2024.124497

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