KGTR:WEIGHTED RECOMMENDATION METHOD BASED ON KNOWLEDGE GRAPH NEIGHBORHOOD EXPANSION FEATURE

The application of the knowledge graph to the recommendation algorithm can effectively reduce the problem of data sparsity and user cold start barriers in the recommendation system. This paper proposes a weighted recommendation method based on the neighborhood expansion feature. This method added the correlation strength between the relationship and the entity to complete the neighborhood representation of the items features in the knowledge graph convolutional network (KGCN). We combined it with the results of the model (RippleNet) that expanded users features using a weighted method. The KL (Kullback-Leibler) divergence was introduced as the deviation. The experiment was carried out on two datasets of MovieLens-1M and Book-Crossing. The results show that the proposed method is improved on AUC, F1 and ACC compared with the baseline model.