Learning Interest-oriented Universal User Representation via Self-supervision

User representation is essential for providing high-quality commercial services in industry. In our business scenarios, we face the challenge of learning universal (general-purpose) user representation. The universal representation is expected to be informative, and can handle various types of real-world applications without fine-tuning (e.g., applicable for both user profiling and the recall process in advertising). It shows great advantages compared to the solution of training a specific model for each downstream application. Specifically, 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. Such an industrial solution has now been deployed in various real-world tasks.