Multi-view Multi-behavior Contrastive Learning in Recommendation

• URL: http://arxiv.org/abs/2203.10576v1
• Date: Sun, 20 Mar 2022 15:13:28 GMT
• Title: Multi-view Multi-behavior Contrastive Learning in Recommendation
• Authors: Yiqing Wu, Ruobing Xie, Yongchun Zhu, Xiang Ao, Xin Chen, Xu Zhang, Fuzhen Zhuang, Leyu Lin, Qing He
• Abstract summary: Multi-behavior recommendation (MBR) aims to jointly consider multiple behaviors to improve the target behavior's performance. We propose a novel Multi-behavior Multi-view Contrastive Learning Recommendation framework.
• Score: 52.42597422620091
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-behavior recommendation (MBR) aims to jointly consider multiple behaviors to improve the target behavior's performance. We argue that MBR models should: (1) model the coarse-grained commonalities between different behaviors of a user, (2) consider both individual sequence view and global graph view in multi-behavior modeling, and (3) capture the fine-grained differences between multiple behaviors of a user. In this work, we propose a novel Multi-behavior Multi-view Contrastive Learning Recommendation (MMCLR) framework, including three new CL tasks to solve the above challenges, respectively. The multi-behavior CL aims to make different user single-behavior representations of the same user in each view to be similar. The multi-view CL attempts to bridge the gap between a user's sequence-view and graph-view representations. The behavior distinction CL focuses on modeling fine-grained differences of different behaviors. In experiments, we conduct extensive evaluations and ablation tests to verify the effectiveness of MMCLR and various CL tasks on two real-world datasets, achieving SOTA performance over existing baselines. Our code will be available on \url{https://github.com/wyqing20/MMCLR}

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