Contrastive Meta Learning with Behavior Multiplicity for Recommendation

• URL: http://arxiv.org/abs/2202.08523v1
• Date: Thu, 17 Feb 2022 08:51:24 GMT
• Title: Contrastive Meta Learning with Behavior Multiplicity for Recommendation
• Authors: Wei Wei and Chao Huang and Lianghao Xia and Yong Xu and Jiashu Zhao and Dawei Yin
• Abstract summary: A well-informed recommendation framework could not only help users identify their interested items, but also benefit the revenue of various online platforms. We propose Contrastive Meta Learning (CML) to maintain dedicated cross-type behavior dependency for different users. Our method consistently outperforms various state-of-the-art recommendation methods.
• Score: 42.15990960863924
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A well-informed recommendation framework could not only help users identify their interested items, but also benefit the revenue of various online platforms (e.g., e-commerce, social media). Traditional recommendation models usually assume that only a single type of interaction exists between user and item, and fail to model the multiplex user-item relationships from multi-typed user behavior data, such as page view, add-to-favourite and purchase. While some recent studies propose to capture the dependencies across different types of behaviors, two important challenges have been less explored: i) Dealing with the sparse supervision signal under target behaviors (e.g., purchase). ii) Capturing the personalized multi-behavior patterns with customized dependency modeling. To tackle the above challenges, we devise a new model CML, Contrastive Meta Learning (CML), to maintain dedicated cross-type behavior dependency for different users. In particular, we propose a multi-behavior contrastive learning framework to distill transferable knowledge across different types of behaviors via the constructed contrastive loss. In addition, to capture the diverse multi-behavior patterns, we design a contrastive meta network to encode the customized behavior heterogeneity for different users. Extensive experiments on three real-world datasets indicate that our method consistently outperforms various state-of-the-art recommendation methods. Our empirical studies further suggest that the contrastive meta learning paradigm offers great potential for capturing the behavior multiplicity in recommendation. We release our model implementation at: https://github.com/weiwei1206/CML.git.

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