Multi-Feature Behavior Relationship for Multi-Behavior Recommendation

• Published in: Applied sciences Vol. 12; no. 24; p. 12909
• Main Authors: Mu, Xiaodong, Zeng, Zhaoju, Shen, Danyao, Zhang, Bo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2022
• Subjects: Decomposition; graph neural network; Learning; multi-behavior recommendation; Neural networks; Recommender systems; Representations; sequence modeling; User behavior
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app122412909

Multi-behavior recommendation aims to model the interaction information of multiple behaviors to enhance the target behavior’s recommendation performance. Despite progress in recent research, it is challenging to represent users’ preferences using the multi-feature behavior information of user interactions. In this paper, we propose a Multi-Feature Behavior Relationship for Multi-Behavior Recommendation (MFBR) framework, which models the multi-behavior recommendation problem from both sequence structure and graph structure perspectives for user preference prediction of target behaviors. Specifically, the MFBR model is designed with a sequence encoder and a graph encoder to construct behavioral representations of different aspects of the user; the correlations between behaviors are modeled by a behavioral relationship encoding layer, and the importance of different behaviors is finally learned in order to construct the final representation of user preferences. Experimental validation conducted on two real-world recommendation datasets shows that our MFBR consistently outperforms state-of-the-art methods.