Interest-oriented Universal User Representation via Contrastive Learning

• URL: http://arxiv.org/abs/2109.08865v1
• Date: Sat, 18 Sep 2021 07:42:00 GMT
• Title: Interest-oriented Universal User Representation via Contrastive Learning
• Authors: Qinghui Sun, Jie Gu, Bei Yang, XiaoXiao Xu, Renjun Xu, Shangde Gao, Hong Liu, Huan Xu
• Abstract summary: We attempt to improve universal user representation from two points of views. A contrastive self-supervised learning paradigm is presented to guide the representation model training. A novel multi-interest extraction module is presented.
• Score: 28.377233340976197
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: User representation is essential for providing high-quality commercial services in industry. Universal user representation has received many interests recently, with which we can be free from the cumbersome work of training a specific model for each downstream application. In this paper, we attempt to improve universal user representation from two points of views. First, a contrastive self-supervised learning paradigm is presented to guide the representation model training. It provides a unified framework that allows for long-term or short-term interest representation learning in a data-driven manner. Moreover, a novel multi-interest extraction module is presented. The module introduces an interest dictionary to capture principal interests of the given user, and then generate his/her interest-oriented representations via behavior aggregation. Experimental results demonstrate the effectiveness and applicability of the learned user representations.

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