A content-based collaborative filtering algorithm for movies and TVS recommendation

Abstract

With the rapid development of multimedia technology and the constant upgrading of film and television libraries, users' demand for movies and television is increasing. How to accurately and timely find favorite movies from massive movie and television resources according to user's preferences and needs has become a great challenge. In recent years, the recommendation of movies and TVs has attracted a lot of research interest from academia and industry. The existing recommendation algorithms mainly include content based and collaborative filtering. The former recommends projects through collaborative learning of others' interests, while the content-based method examines the rich context of the project. In this paper, to further improve the performance of recommendations, a content based collaborative filtering method is proposed to provide recommendations for movies and television. Specifically, we extract and vectorize feature and category information from movies based on TF-IDF and apply truncated SVD to reduce the dimensions of the rating and TF-IDF matrix to retain the most representative information. We calculate the cosine similarity between the vectors from these two matrices. The final recommendation is to list 10 movies based on the average similarity of content and ratings. Extensive experiments on Amazon review data have proven the effectiveness of this method.