Movie recommendation algorithm based on knowledge graph

The traditional collaborative filtering algorithm only uses the item-user scoring matrix, but does not consider the semantics of the item. The recommendation effect is often not ideal. The connotation knowledge of the movie and the interest preference of the user are added to the knowledge graph, wherein the connotation knowledge is represented by the relationship between the movie entity and its characteristic entity, and the user's interest preference is to use the like relationship and the like relationship between the movie entity and the user entity. Said. After adding the intension knowledge and the user's interest preferences in the knowledge graph, the complex and diverse relationship between the entities is represented in the form of a triple. The knowledge graph is used to represent the learning method, the movie knowledge graph is embedded in a low-dimensional semantic space, the movie entity is vectorized, and then the semantic similarity between the movie entities is calculated, similar to the item-based collaborative filtering. Sexual combination, the film's own connotation information and user preferences in the knowledge graph are integrated into the collaborative filtering for recommendation, which can not only make up for the problem of semantic sparsity, but also can solve the lack of user subjectivity of the knowledge graph because the knowledge graph contains the user's subjective interest preferences. In the knowledge graph, both the objectivity of the connotation knowledge of the film itself and the subjectivity of the user's interest are considered. Compared with the connotation knowledge of the film itself in the knowledge graph, user preferences and object-based items are added to the knowledge graph. The synergistic filtering fusion enhances the recommended effect.