MPL-TransKR: Multi-Perspective Learning Based on Transformer Knowledge Graph Enhanced Recommendation

Abstract

The emergence of recommender system is aimed at solving the problems brought by information explosion to human life and even the development of human society. As a traditional recommendation technique, collaborative filtering often encounters sparsity and cold start problems in many recommendation scenarios. Therefore, researchers have found that the introduction of side information can solve these problems to a certain extent and improve the performance of recommender systems. The knowledge graph is a heterogeneous graph that contains rich semantic relationships among items. The Multi-Perspective Learning based on Transformer Knowledge Graph Enhanced Recommendation (MPL-TransKR) proposed in this paper uses the knowledge graph as the side information for input and introduces the multihead self-attention mechanism by reasonably combining the transformer idea. While learning the high-order neighborhood information of the items, the long-distance information between the items is captured, and the weight value is assigned to the user through the attention mechanism to strengthen the user representation to realize user-item multi-perspective learning to enhance the performance of the recommendation model. Through extensive experiments using public datasets, we demonstrated that MPL-TransKR performs well in book and music recommendations, surpassing state-of-the-art baselines on several metrics.