Weixin Chen
ABSTRACT
Heterogeneous sequential recommendation (HSR) is a very important recommendation problem, which aims to predict a user’s next interacted item under a target behavior type (e.g., purchase in e-commerce sites) based on his/her historical interactions with different behaviors. Though existing sequential methods have achieved advanced performance by considering the varied impacts of interactions with sequential information, a large body of them still have two major shortcomings. Firstly, they usually model different behaviors separately without considering the correlations between them. The transitions from item to item under diverse behaviors indicate some users’ potential behavior manner. Secondly, though the behavior information contains a user’s fine-grained interests, the insufficient consideration of the local context information limits them from well understanding user intentions. Utilizing the adjacent interactions to better understand a user’s behavior could improve the certainty of prediction. To address these two issues, we propose a novel solution utilizing global and personalized graphs for HSR (GPG4HSR) to learn behavior transitions and user intentions. Specifically, our GPG4HSR consists of two graphs, i.e., a global graph to capture the transitions between different behaviors, and a personalized graph to model items with behaviors by further considering the distinct user intentions of the adjacent contextually relevant nodes. Extensive experiments on four public datasets with the state-of-the-art baselines demonstrate the effectiveness and general applicability of our method GPG4HSR.
Supplemental Material
- Chong Chen, Weizhi Ma, Min Zhang, Zhaowei Wang, Xiuqiang He, Chenyang Wang, Yiqun Liu, and Shaoping Ma. 2021. Graph Heterogeneous Multi-Relational Recommendation. In AAAI’21. 3958–3966.Google Scholar
- Alexander Dallmann, Daniel Zoller, and Andreas Hotho. 2021. A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models. In RecSys’21. 505–514.Google Scholar
- William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NeurIPS’17. 1024–1034.Google Scholar
- Mingkai He, Weike Pan, and Zhong Ming. 2022. BAR: Behavior-Aware Recommendation for Sequential Heterogeneous One-Class Collaborative Filtering. Information Sciences 608(2022), 881–899.Google Scholar
- Ruining He, Wang-Cheng Kang, and Julian McAuley. 2017. Translation-based recommendation. In RecSys’17. 161–169.Google Scholar
- Ruining He and Julian McAuley. 2016. Fusing similarity models with Markov chains for sparse sequential recommendation. In ICDM’16. 191–200.Google Scholar
- Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yong-Dong Zhang, and Meng Wang. 2020. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In SIGIR’20. 639–648.Google Scholar
- Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016. Session-based Recommendations with Recurrent Neural Networks. In ICLR’16.Google Scholar
- Santosh Kabbur, Xia Ning, and George Karypis. 2013. FISM: Factored Item Similarity Models for top-N Recommender Systems. In KDD’13. 659–667.Google ScholarDigital Library
- Wang-Cheng Kang and Julian J. McAuley. 2018. Self-Attentive Sequential Recommendation. In ICDM’18. 197–206.Google Scholar
- 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 KDD’18. 1734–1743.Google Scholar
- Feng Liang, Enyue Yang, Weike Pan, Qiang Yang, and Zhong Ming. 2022. Survey of Recommender Systems Based on Federated Learning (in Chinese). SCIENTIA SINICA Informationis 52(5) (2022), 713–741.Google Scholar
- Jing Lin, Weike Pan, and Zhong Ming. 2020. FISSA: Fusing Item Similarity Models with Self-Attention Networks for Sequential Recommendation. In RecSys’20. 130–139.Google Scholar
- Zhaohao Lin, Weike Pan, Qiang Yang, and Zhong Ming. 2022. Recommendation Framework via Fake Marks and Secret Sharing. ACM Transactions on Information Systems(2022).Google Scholar
- Kelong Mao, Jieming Zhu, Xi Xiao, Biao Lu, Zhaowei Wang, and Xiuqiang He. 2017. UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation. In CIKM’21. 1253–1262.Google Scholar
- Wenjing Meng, Deqing Yang, and Yanghua Xiao. 2020. Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation. In SIGIR’20. 1091–1100.Google Scholar
- Wenhao Pan and Kai Yang. 2021. Multi-behavior Graph Neural Networks for Session-based Recommendation(MLDBBI’21). 756–761.Google Scholar
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In UAI’09. 452–461.Google Scholar
- Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized Markov chains for next-basket recommendation. In WWW’10. 811–820.Google Scholar
- Qi Shen, Lingfei Wu, Yitong Pang, Yiming Zhang, Zhihua Wei, Fangli Xu, and Bo Long. 2021. Multi-behavior Graph Contextual Aware Network for Session-based Recommendation. CoRR abs/2109.11903(2021).Google Scholar
- Jiaxi Tang and Ke Wang. 2018. Personalized top-N Sequential Recommendation via Convolutional Sequence Embedding. In WSDM’18. 565–573.Google Scholar
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All You Need. In NeurIPS’17. 6000–6010.Google Scholar
- Wen Wang, Wei Zhang, Shukai Liu, Qi Liu, Bo Zhang, Leyu Lin, and Hongyuan Zha. 2020. Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction. In WWW’20. 3056–3062.Google Scholar
- Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural Graph Collaborative Filtering. In SIGIR’19. 165–174.Google Scholar
- Ziyang Wang, Wei Wei, Gao Cong, Xiao-Li Li, Xianling Mao, and Minghui Qiu. 2020. Global Context Enhanced Graph Neural Networks for Session-based Recommendation. In SIGIR’20. 169–178.Google Scholar
- Wei Wei, Chao Huang, Lianghao Xia, Yong Xu, Jiashu Zhao, and Dawei Yin. 2022. Contrastive Meta Learning with Behavior Multiplicity for Recommendation. In WSDM’22. 1120–1128.Google Scholar
- Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. 2019. Session-based Recommendation with Graph Neural Networks. In AAAI’19. 346–353.Google Scholar
- Lianghao Xia, Yong Xu, Chao Huang, Peng Dai, and Liefeng Bo. 2021. Graph Meta Network for Multi-Behavior Recommendation. In SIGIR’21. 757–766.Google Scholar
- Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Victor S Sheng, Jiajie Xu, Fuzhen Zhuang, Junhua Fang, and Xiaofang Zhou. 2019. Graph Contextualized Self-Attention Network for Session-based Recommendation. In IJCAI’19. 3940–3946.Google Scholar
- Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec. 2018. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. In KDD’18. 974–983.Google Scholar
- Zhuoxin Zhan, Mingkai He, Weike Pan, and Zhong Ming. 16(2):162615, 2022. TransRec++: Translation-based Sequential Recommendation with Heterogeneous Feedback. FCS (16(2):162615, 2022). https://doi.org/fcs/EN/10.1007/s11704-022-1184-8Google Scholar
- Meizi Zhou, Zhouye Ding, Jiliang Tang, and Dawei Yin. 2018. Micro Behaviors: A New Perspective in E-commerce Recommender Systems. In WSDM’18. 727–735.Google ScholarDigital Library
Index Terms
- Global and Personalized Graphs for Heterogeneous Sequential Recommendation by Learning Behavior Transitions and User Intentions
Recommendations
User Popularity Preference Aware Sequential Recommendation
Computational Science – ICCS 2023AbstractIn recommender systems, users’ preferences for item popularity are diverse and dynamic, which reveals the different items that users prefer. Therefore, identifying user popularity preferences are significant for personalized recommendations. ...
Improving Sequential Recommendation with Attribute-Augmented Graph Neural Networks
Advances in Knowledge Discovery and Data MiningAbstractMany practical recommender systems provide item recommendation for different users only via mining user-item interactions but totally ignoring the rich attribute information of items that users interact with. In this paper, we propose an attribute-...
Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation
WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data MiningPredicting the next interaction of a short-term interaction session is a challenging task in session-based recommendation. Almost all existing works rely on item transition patterns, and neglect user historical sessions while modeling user preference, ...
Comments