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Multi-Behavior Sequential Recommendation With Temporal Graph Transformer

Publication ,  Journal Article
Xia, L; Huang, C; Xu, Y; Pei, J
Published in: IEEE Transactions on Knowledge and Data Engineering
June 1, 2023

Modeling time-evolving preferences of users with their sequential item interactions, has attracted increasing attention in many online applications. Hence, sequential recommender systems have been developed to learn the dynamic user interests from the historical interactions for suggesting items. However, the interaction pattern encoding functions in most existing sequential recommender systems have focused on single type of user-item interactions. In many real-life online platforms, user-item interactive behaviors are often multi-typed (e.g., click, add-to-favorite, purchase) with complex cross-type behavior inter-dependencies. Learning from informative representations of users and items based on their multi-typed interaction data, is of great importance to accurately characterize the time-evolving user preference. In this work, we tackle the dynamic user-item relation learning with the awareness of multi-behavior interactive patterns. Towards this end, we propose a new Temporal Graph Transformer (TGT) recommendation framework to jointly capture dynamic short-term and long-range user-item interactive patterns, by exploring the evolving correlations across different types of behaviors. The new TGT method endows the sequential recommendation architecture to distill dedicated knowledge for type-specific behavior relational context and the implicit behavior dependencies. Experiments on the real-world datasets indicate that our method TGT consistently outperforms various state-of-the-art recommendation methods. Our model implementation codes are available at https://github.com/akaxlh/TGT.

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Published In

IEEE Transactions on Knowledge and Data Engineering

DOI

EISSN

1558-2191

ISSN

1041-4347

Publication Date

June 1, 2023

Volume

35

Issue

6

Start / End Page

6099 / 6112

Related Subject Headings

  • Information Systems
  • 46 Information and computing sciences
  • 08 Information and Computing Sciences
 

Citation

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Xia, L., Huang, C., Xu, Y., & Pei, J. (2023). Multi-Behavior Sequential Recommendation With Temporal Graph Transformer. IEEE Transactions on Knowledge and Data Engineering, 35(6), 6099–6112. https://doi.org/10.1109/TKDE.2022.3175094
Xia, L., C. Huang, Y. Xu, and J. Pei. “Multi-Behavior Sequential Recommendation With Temporal Graph Transformer.” IEEE Transactions on Knowledge and Data Engineering 35, no. 6 (June 1, 2023): 6099–6112. https://doi.org/10.1109/TKDE.2022.3175094.
Xia L, Huang C, Xu Y, Pei J. Multi-Behavior Sequential Recommendation With Temporal Graph Transformer. IEEE Transactions on Knowledge and Data Engineering. 2023 Jun 1;35(6):6099–112.
Xia, L., et al. “Multi-Behavior Sequential Recommendation With Temporal Graph Transformer.” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 6, June 2023, pp. 6099–112. Scopus, doi:10.1109/TKDE.2022.3175094.
Xia L, Huang C, Xu Y, Pei J. Multi-Behavior Sequential Recommendation With Temporal Graph Transformer. IEEE Transactions on Knowledge and Data Engineering. 2023 Jun 1;35(6):6099–6112.

Published In

IEEE Transactions on Knowledge and Data Engineering

DOI

EISSN

1558-2191

ISSN

1041-4347

Publication Date

June 1, 2023

Volume

35

Issue

6

Start / End Page

6099 / 6112

Related Subject Headings

  • Information Systems
  • 46 Information and computing sciences
  • 08 Information and Computing Sciences