Behavior Merging Graph Convolution Network for Multi-Behavior Recommendation
Chen, Hao Li, Zhiqing Bei, Yuanchen Xu, Kai Zhang, Yijie Huang, Feiran Yang, Yu Gong, Huan Karray, Fakhri
Chen, Hao
Li, Zhiqing
Bei, Yuanchen
Xu, Kai
Zhang, Yijie
Huang, Feiran
Yang, Yu
Gong, Huan
Karray, Fakhri
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Supervisor
Department
Machine Learning
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Type
Journal article
Date
2025
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Language
English
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Abstract
Encoding multi-behavior information into a single graph collaborative filtering vector is an emerging challenge, as different behaviors generate distinct graphs, each with its own embedding vector. To address this problem, recent approaches typically designate the embedding of some behaviors as primary embeddings and use the embeddings of other behaviors to enhance the primary behavior recommendation. However, these models may excel in recommending primary behaviors at the expense of degrading the performance of auxiliary behaviors. As a result, modern recommender systems often need to maintain multiple sets of collaborative filtering embeddings to achieve satisfactory recommendation performance across all behaviors. To alleviate this issue, we introduce the Behavior Merging Graphs. Instead of modeling each behavior separately, BMG uses a joint graph to capture potential behavior merging sets between nodes and applies the partial order theory to model the intricate structures and relational order among behavior merging sets. Based on BMG, we introduce the Behavior Merging Graphs Convolutional Networks (BMGCN), which aggregates neighbor information by integrating convolutional weights that account for the rank transformation of Behavior Merging Order across various behavior merging sets. Furthermore, BMGCN employs behavior merging-based sampling to guide the traditional BPR sampling process, enhancing embedding training. Experiments on three widely used datasets demonstrate that BMGCN achieves superior multi-behavior recommendation performance compared to state-of-the-art baselines.
Citation
H. Chen et al., "Behavior Merging Graph Convolution Network for Multi-Behavior Recommendation," in IEEE Transactions on Knowledge and Data Engineering, doi: 10.1109/TKDE.2025.3618466.
Source
IEEE Transactions on Knowledge and Data Engineering
Conference
Keywords
Graph Collaborative Filtering, Multi-Behavior Recommendation, Recommender Systems
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Publisher
IEEE