A Recommendation Algorithm Incorporating Self-Attention Mechanism and Knowledge Graph

To address the problems of sparse data, low recommendation accuracy and poor recommendation effect in recommendation systems. In this paper, we propose a recommendation algorithm that fuses the self-attention mechanism and knowledge graph. The algorithm mainly includes recommendation module, knowledge graph feature learning, and self-attention. In this algorithm recommendation system module, a user and an item are input, and the input item vector and entity vector are embedded in the self-attention module, so that the feature representation of these two vectors is enhanced. The knowledge graph feature representation module maps the head entities and relations in the triad into a continuous vector space, and calculates the corresponding values through the score function. The recommendation module and the knowledge graph representation model are connected through the cross-compression unit embedded in the self-attentive mechanism. Finally, the loss of each module is calculated by a loss function. Experiments on three different publicly available datasets show that: the embedded attention mechanism module introduced can well solve the accuracy problem of the recommendation system; Secondly, the embedded attention mechanism cross-compression unit module enhances the recommendation system in which vectors are compressed in horizontal and vertical directions. Finally, through experiments comparing other algorithms, the proposed method improves the recommendation accuracy and effectiveness in the recommendation system.