Abstract: Collaborative Filtering Algorithm(CF) makes recommendations based on the user-item scoring matrix, without considering the item’s own attributes. In this paper, the movie attributes on the MovieLens dataset are used as factors influencing the recommendation results, and are combined with various factors such as the introduction, comments, ratings, directors, and actors of the movie. CNN (Convolutional Neural Network) and Word2Vec (Word to Vector) word vector model are used to process the movie introduction; AFINN (Finn rup Nielsen Emotion Dictionary) is used to process the comments and the results are mapped; the director and actor data are modeled to get the prediction score under the factors, and finally the results under the various factors are weighted and combined, and the weight is adjusted to obtain the best effect. It is verified that the recommended performance of this method is better than the traditional CF algorithm.

Key words:  multi-factor, fusion, film, recommendation system