Optimization of Collaborative Filtering Algorithm in Movie Recommendation System

Abstract

In the era of big data information explosion, people are faced with a large amount of information every day, and how to obtain the required content in a large amount of information. The appearance of intelligent recommendation system has brought great convenience to our life. The recommendation system can recommend corresponding functions, products and services according to users’ past browsing information, enabling users to get their desired information data from massive data more efficiently. As an indispensable part of most people’s entertainment life, movie recommendation has also become a very important part of Internet recommendation content. The collaborative filtering algorithm is used to realize the personalized recommendation of movies. However, in the process of movie recommendation, it is found that new users are only recommended movies based on their ratings without considering the attribute information between movies, which may lead to problems such as inaccurate recommendation accuracy. Therefore, this paper further optimizes the collaborative filtering algorithm and introduces the similarity calculation between movie attributes to improve the accuracy of movie recommendation.