A Book Recommendation Algorithm Based on Improved Similarity Calculation

Rcommendation system can solve the information overload in mass data and recommend content that users are interested in. User similarity calculation is a common recommendation algorithm, but the traditional algorithm only considers the similarity between user-item ratings and ignores the influence of users' inherent characteristics. This paper presents an algorithm combining user feature similarity and user-item rating similarity, and proposes to use F1 indicator to evaluate the efficiency of the recommendation algorithm. The hexperimental results show that the improved algorithm can effectively improve the recommendation effect.