Interest-oriented Universal User Representation via Contrastive Learning

TL;DR

This paper introduces a contrastive self-supervised learning framework combined with a multi-interest extraction module to enhance universal user representations, enabling better interest modeling for diverse downstream applications.

Contribution

It proposes a novel interest-oriented universal user representation method that integrates contrastive learning with an interest dictionary for improved interest capture.

Findings

Effective in capturing long-term and short-term interests

Improves performance of downstream tasks

Demonstrates applicability across various scenarios

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.