A cascaded embedding method with graph neural network for multi-behavior recommendation
International Journal of Machine Learning and Cybernetics - Trang 1-14 - 2023
Tóm tắt
In recommender systems, implicit feedback data is relatively cheap and easy to obtain compared to explicit feedback data, making it widely used in modeling. However, some works consider only single type of user behavior, while in reality, user feedback types are complex and diverse, with great semantic uncertainty. Graph neural networks (GNN) have gradually become a new paradigm in the recommendation field due to their excellent information extraction capabilities and good scalability. The common recommendation models based on GNN have high time complexity and ignore the contribution of different behaviors to user preferences. To address these challenges, we propose a Cascading Embedding method for Multi-Behavior Recommendation to explore high-order multi-relation interaction signals between users and items. Specifically, we associate different user behaviors in a specific order and design a relation-aware gating unit to extract user behavior patterns, learn node (user and item) and relation representations. To investigate the differential effects of different types of behavior on different users, a relation-level attention mechanism is proposed to automatically capture the importance of each behavior to user preferences. Finally, we perform the non-sampling optimization strategy based on the multi-task learning framework to fully utilize auxiliary behaviors in better predicting target behaviors. Experimental results on real datasets demonstrate that the proposed model outperforms current mainstream recommendation methods. Further analysis and verification show that multi-behavior modeling can provide more effective recommendations for users with sparse target behaviors. Our implementation code is available in
https://github.com/jsp666/CEMBR
.
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