A movie recommendation algorithm based on knowledge graph and collaborative filtering.

Aiming at the problem that the collaborative filtering recommendation algorithm can only consider the external reviews of movies and not the similarity relationships within movies during the process of recommending movies, the paper proposes to construct a knowledge graph to help calculate the internal similarity of movies. Existing movie data may be incomplete, so knowledge graph reasoning is used to complement missing movie knowledge. The knowledge graph based on TransE model cannot effectively describe the complex multi-relationship among movie titles, actors and directors. Firstly, the improved TransHR model can express the multi-relationship between movie information and improve the accuracy of relationship representation. Then, the similarity between movies is calculated by the user rating matrix. Finally, the two similarities are merged and applied to the recommended technique of matrix decomposition. The experimental results show that the algorithm improves Recall, Precision, and MAE. [ABSTRACT FROM AUTHOR]