Recommendation of Knowledge Graph Convolutional Networks Based on Multilayer BiLSTM and Self-Attention

Abstract

To solve the problems of cold start, sparse data, and poor recommendation performance in collaborative filtering recommendation, an end-to-end framework algorithm based on BiLSTM and BAGCN was proposed. In order to discover the higher-order structural information in the knowledge graph, stacked BiLSTM is used to extract the features of embedded entities and relationships, respectively, and the depth dependence features of user-item interaction matrix are mined. The neighborhood representation of each entity is then calculated by sampling adjacent entities of a fixed size. Then, the self-attention mechanism is used to learn the semantic association between entities and neighboring entities to obtain the final neighborhood information. Aggregators are used to combine neighborhood information and bias information when computing node representations. By extending the sampling of adjacent entities to multihop simulation of higher-order adjacent information, users' potential long-distance interests can be captured. Compared with the baseline model, the superiority of this method is verified.