Interest-oriented Universal User Representation via Contrastive Learning
This work addresses the need for efficient user modeling in industry to avoid training separate models for each application, though it appears incremental.
The paper tackles the problem of universal user representation for commercial services by introducing a contrastive self-supervised learning framework and a multi-interest extraction module, resulting in improved representations that show effectiveness in experiments.
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.