Abstract
Contrastive learning (CL) has shown its power in recommendation. However, most CL-based models build their CL tasks merely focusing on the user’s aspects, ignoring systematically modeling the rich and diverse information in items. In this work, we propose a novel Multi-granularity item-based contrastive learning (MicRec) for the matching stage in recommendation, which systematically introduces multi-aspect item-related correlations to representation learning via CL. Specifically, we build three item-based CL tasks as a set of plug-and-play auxiliary objectives to capture item correlations in feature, semantic and session levels. In experiments, we conduct both offline and online evaluations on real-world datasets, verifying the effectiveness and universality of three proposed CL tasks. Currently, MicRec has been deployed on a real-world recommender system of WeChat Top Stories, affecting millions of users.
R. Xie and Z. Qiu—Authors have equal contributions.
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Xie, R., Qiu, Z., Zhang, B., Lin, L. (2023). Multi-granularity Item-Based Contrastive Recommendation. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13944. Springer, Cham. https://doi.org/10.1007/978-3-031-30672-3_27
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DOI: https://doi.org/10.1007/978-3-031-30672-3_27
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