A Time and Relation-Aware Graph Collaborative Filtering for Cross-Domain Sequential Recommendation

Ren Hao; Liu Baisong; Sun Jinyang; Dong Qian; Qian Jiangbo

doi:10.7544/issn1000-1239.202110545

Journal of Computer Research and Development > 2023 > 60(1): 112-124. > DOI: 10.7544/issn1000-1239.202110545

Ren Hao, Liu Baisong, Sun Jinyang, Dong Qian, Qian Jiangbo. A Time and Relation-Aware Graph Collaborative Filtering for Cross-Domain Sequential Recommendation[J]. Journal of Computer Research and Development, 2023, 60(1): 112-124. DOI: 10.7544/issn1000-1239.202110545

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A Time and Relation-Aware Graph Collaborative Filtering for Cross-Domain Sequential Recommendation

Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang 315211

Funds: This work was supported by the National Natural Science Foundation of China (62271274), the Natural Science Foundation of Zhejiang Province (LZ20F020001), the 2025 Major Special Scientific Research Project of Ningbo City (20211ZDYF020036), and the Natural Science Foundation of Ningbo City (2021J091).

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Received Date: June 02, 2021
Revised Date: June 06, 2022
Available Online: February 10, 2023

Graphical Abstract

Abstract

Abstract

Cross-domain sequential recommendation aims to mine a given user’s preferences from the historical interaction sequences in different domains and to predict the next item that the user is most likely to interact with among multiple domains, further to mitigate the impact of data sparsity on the capture and prediction for users’ intents. Inspired by the idea of collaborative filtering, a time and relation-aware graph collaborative filtering for cross-domain sequential recommendation (TRaGCF) algorithm is proposed to solve the problem of data sparsity by uncovering users’ high-order behavior patterns as well as utilizing the characteristics of bi-directional migration of user behavior patterns across domains. Firstly, we propose a time-aware graph attention (Ta-GAT) mechanism to obtain the cross-domain sequence-level item representation. Then, a user-item interaction bipartite graph in the domain is used to mine users’ preferences, and a relation-aware graph attention (Ta-GAT) mechanism is proposed to learn item collaborative representation and user collaborative representation, which creates the foundation for cross-domain transfer of user preferences. Finally, to simultaneously improve the recommendation results in both domains, a user preference feature bi-directional transfer module (PBT) is proposed, transferring shared user preferences across domains and retaining specific preferences within one domain. The accuracy and effectiveness of our model are validated by two experimental datasets, Amazon Movie-Book and Food-Kitchen. The experimental results have demonstrated the necessity of considering intricate correlations between items in a cross-domain sequential recommendation scenario for mining users’ intents, and the results also prove the importance of preserving users’ specific preferences in creating a comprehensive user portrait when transferring users’ preferences across domains.

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References (38)

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