Fu-Rec: Multi-Task Learning Recommendation Model Fusing Neighbor-Discrimination and Self-Discrimination

Abstract

In recent years, self-supervised learning has achieved great success in areas such as computer vision and natural language processing because it can mine supervised signals from unlabeled data and reduce the reliance on manual labels. However, the currently generated self-supervised signals are either neighbor discrimination or self-discrimination, and there is no model to integrate neighbor discrimination and self-discrimination. Based on this, this paper proposes Fu-Rec that integrates neighbor-discrimination contrastive learning and self-discrimination contrastive learning, which consists of three modules: (1) neighbor-discrimination contrastive learning, (2) self-discrimination contrastive learning, and (3) recommendation module. The neighbor-discrimination contrastive learning and self-discrimination contrastive learning tasks are used as auxiliary tasks to assist the recommendation task. The Fu-Rec model effectively utilizes the respective advantages of neighbor-discrimination and self-discrimination to consider the information of the user's neighbors as well as the user and the item itself for the recommendation, which results in better performance of the recommendation module. Experimental results on several public datasets demonstrate the effectiveness of the Fu-Rec proposed in this paper.