Hybrid Movie Recommendation System with Content-Based and Memory-Based Collaborative Filtering Using Similarity Graph

The recommendation system can help people filter out the data that users may like from the massive data, and the recommendation algorithm based on content and collaborative filtering has been widely used in the current recommendation field. Still, the recommendation result of the content-based algorithm is single and lacks innovation. However, the collaborative filtering algorithm reduces the recommendation accuracy due to the sparsity problem. In this paper, we propose a movie recommendation system based on the knowledge graph and a mixture of content-based and collaborative filtering, which will wander the path of the knowledge graph and calculate the recommendation index of movies to users in each path. Get the recommended list of the top N movies by sorting and calculating the Hit Rate and Average Reciprocal Hit Rank. The results show that the proposed recommendation model has good recommendation efficiency, where the best Hit rate and Average Reciprocal Hit Rank are 0.74 and 0.44.