Improvement of Collaborative Filtering Algorithm Based on University Book Recommendation System

Abstract

In view of a series of problems in college book recommendation, such as cold start of users, high proportion of popular recommendations and low recommendation accuracy, this paper improves these problems based on the problems of the existing collaborative filtering algorithm and combined with the characteristics of college book borrowing. An improved University book recommendation algorithm is proposed, in which the time attenuation factor is added when generating the user evaluation matrix, and the influence of gender and professional factors on the user feature similarity is considered. The algorithm solves the problem of insufficient score of collaborative filtering algorithm. Experiments show that this algorithm is better than the traditional collaborative filtering recommendation algorithm and can meet the actual needs.