A multi-task learning model for recommendation based on fusion of dynamic and static neighbors

Abstract

To improve recommendation performance, this study introduces self-supervised learning into recommendation systems and proposes a multi-task learning recommendation framework that combines static neighbor and dynamic neighbor contrastive learning. Specifically, this study considers node relationships at both the graph and embedding levels, which can be defined in two aspects: (1) Static neighbors, which are positive nodes obtained by integrating information from user-item interaction graphs and social graphs. (2) Dynamic neighbors refer to nodes at the encoding level that are similar, but not necessarily the same, at each iteration; hence, they are termed dynamic neighbors. By employing static neighbor contrastive learning and dynamic neighbor contrastive learning as auxiliary tasks for the main recommendation task, we optimized the user and item embeddings. Through extensive experiments using multiple real datasets, we validate the effectiveness of the proposed approach and its components.