Research on Collaborative Filtering Recommendation Algorithm for Improving User Similarity Calculation

The structural similarity and numerical similarity in the similarity calculation of the traditional user-based collaborative filtering recommendation algorithm have their own shortcomings. Structural similarity only uses the structural information of user ratings without considering the numerical information for user similarity, and numerical similarity only uses the ratings to calculate the similarity without considering the structural information, which makes the similarity calculation lacks accuracy. Aiming at this problem, a method that considers both the structural similarity of numerical information and the numerical similarity of structural information is proposed. When improving the numerical similarity, the influence of item quality and item popularity on the numerical similarity is considered. Simultaneously, the commonly used numerical similarity calculation uses the improved structural similarity to optimize. The five-fold cross-validation method is tested on the Movielens100K public data set. Compared with the traditional user-based collaborative filtering recommendation algorithm, the experiment proves that the proposed method effectively improves the recommendation performance.