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Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation

Publication ,  Conference
Xia, L; Huang, C; Xu, Y; Dai, P; Zhang, X; Yang, H; Pei, J; Bo, L
Published in: 35th AAAI Conference on Artificial Intelligence, AAAI 2021
January 1, 2021

Accurate user and item embedding learning is crucial for modern recommender systems. However, most existing recommendation techniques have thus far focused on modeling users’ preferences over singular type of user-item interactions. Many practical recommendation scenarios involve multi-typed user interactive behaviors (e.g., page view, add-to-favorite and purchase), which presents unique challenges that cannot be handled by current recommendation solutions. In particular: i) complex inter-dependencies across different types of user behaviors; ii) the incorporation of knowledge-aware item relations into the multi-behavior recommendation framework; iii) dynamic characteristics of multi-typed user-item interactions. To tackle these challenges, this work proposes a Knowledge-Enhanced Hierarchical Graph Transformer Network (KHGT), to investigate multi-typed interactive patterns between users and items in recommender systems. Specifically, KHGT is built upon a graph-structured neural architecture to i) capture type-specific behavior characteristics; ii) explicitly discriminate which types of user-item interactions are more important in assisting the forecasting task on the target behavior. Additionally, we further integrate the graph attention layer with the temporal encoding strategy, to empower the learned embeddings be reflective of both dedicated multiplex user-item and item-item relations, as well as the underlying interaction dynamics. Extensive experiments conducted on three real-world datasets show that KHGT consistently outperforms many state-of-the-art recommendation methods across various evaluation settings. Our implementation code is available in https://github.com/akaxlh/KHGT.

Duke Scholars

Published In

35th AAAI Conference on Artificial Intelligence, AAAI 2021

ISBN

9781713835974

Publication Date

January 1, 2021

Volume

5B

Start / End Page

4486 / 4493
 

Citation

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Xia, L., Huang, C., Xu, Y., Dai, P., Zhang, X., Yang, H., … Bo, L. (2021). Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation. In 35th AAAI Conference on Artificial Intelligence, AAAI 2021 (Vol. 5B, pp. 4486–4493).
Xia, L., C. Huang, Y. Xu, P. Dai, X. Zhang, H. Yang, J. Pei, and L. Bo. “Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation.” In 35th AAAI Conference on Artificial Intelligence, AAAI 2021, 5B:4486–93, 2021.
Xia L, Huang C, Xu Y, Dai P, Zhang X, Yang H, et al. Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation. In: 35th AAAI Conference on Artificial Intelligence, AAAI 2021. 2021. p. 4486–93.
Xia, L., et al. “Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation.” 35th AAAI Conference on Artificial Intelligence, AAAI 2021, vol. 5B, 2021, pp. 4486–93.
Xia L, Huang C, Xu Y, Dai P, Zhang X, Yang H, Pei J, Bo L. Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation. 35th AAAI Conference on Artificial Intelligence, AAAI 2021. 2021. p. 4486–4493.

Published In

35th AAAI Conference on Artificial Intelligence, AAAI 2021

ISBN

9781713835974

Publication Date

January 1, 2021

Volume

5B

Start / End Page

4486 / 4493