Meta Auxiliary Learning for Top-K Recommendation

Abstract

Recommender systems are playing a significant role in modern society to alleviate the information/choice overload problem, since Internet users may feel hard to identify the most favorite items or products from millions of candidates. Thanks to the recent successes in computer vision, auxiliary learning has become a powerful means to improve the performance of a target (primary) task. Even though helpful, the auxiliary learning scheme is still less explored in recommendation models. To integrate the auxiliary learning scheme, we propose a novel meta auxiliary learning framework to facilitate the recommendation model training, i.e., user and item latent representations. Specifically, we construct two self-supervised learning tasks, regarding both users and items, as auxiliary tasks to enhance the representation effectiveness of users and items. Then the auxiliary and primary tasks are further modeled as a meta learning paradigm to adaptively control the contribution of auxiliary tasks for improving the primary recommendation task. This is achieved by an implicit gradient method guaranteeing less time complexity compared with conventional meta learning methods. Via a comparison using four real-world datasets with a number of state-of-the-art methods, we show that the proposed model outperforms the best existing models on the Top-K recommendation by 3% to 23%.