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Learning from the Future for Sequential Recommendation

Published: 30 July 2024 Publication History

Abstract

Modeling sequential behaviors is the core of sequential recommendation. As users visit items in chronological order, existing methods typically capture a user’s present interests from his/her past-to-present behaviors, i.e., making recommendations with only the unidirectional past information. This paper argues that future information is another critical factor for the sequential recommendation and can be learned from users’ collaborative behaviors. Toward this end, this paper introduces sequential graphs to depict item transition relationships: where and how each item transits from and will transit to. Then, a Bidirectional Sequential Graph Convolutional Network (BiSGCN) is proposed to encode the temporal evolving information in the past and future. Finally, a Manifold Translating Embedding (MTE) method is proposed to capture the geometric structures of item transition patterns. Experimental comparisons and ablation studies verify the outstanding performance of BiSGCN, the benefits of learning from the future, and the improvements of learning in Riemannian manifolds.

References

[1]
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. 452–461.
[2]
Zhongchuan Sun, Bin Wu, Yifan Chen, and Yangdong Ye. 2023. Learning From the Future: Light Cone Modeling for Sequential Recommendation. IEEE Transactions on Cybernetics 53, 8 (2023), 5358–5371.
[3]
Zhongchuan Sun, Bin Wu, Youwei Wang, and Yangdong Ye. 2022. Sequential Graph Collaborative Filtering. Information Sciences 592 (2022), 244–260.

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ACM-TURC '24: Proceedings of the ACM Turing Award Celebration Conference - China 2024
July 2024
261 pages
ISBN:9798400710117
DOI:10.1145/3674399
This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

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Published: 30 July 2024

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