Self-supervised cognitive learning for multifaced interest in large-scale industrial recommender systems

Abstract

Recommender systems play a vital role in e-commerce. Existing algorithms usually learn ranking scores from a user's historical sequence, but they generally pay little attentions to mining the user's precise and varied interests to jointly optimize multiple tasks. In this study, we argue that it is crucial to formulate different user interests based on self-supervised learning (SSL) enhanced data representation for multi-scene objectives. We propose self-supervised cognitive learning (SSCL), a novel framework that can simultaneously model a user's multifaceted interest representation and linked multitask information learning. SSCL exploits SSL to enhance the value of training data. In addition, multi-interest-aggregation and multi-interest-select units are introduced and a customized gate-control model is used to optimize multiple objectives. We evaluate our framework using three real-world datasets, and our results demonstrate its effectiveness and superior performance over the state-of-the-art techniques. Compared with the baselines in three datasets, the click-through rate increased by +7.58%, and the conversion rate increased by +6.04%.