A Collaborative Filtering Recommendation Algorithm with Improved Similarity Calculation

Abstract

Abstract-In order to improve the accuracy of the proposed algorithm in collaborative filtering recommendation system, an Improved Pearson collaborative filtering (IP-CF) algorithm is proposed in this paper. The algorithm uses the user portrait, item characteristics and data of user behavior to compute the baseline predictors model. Instead of the traditional algorithm's similarity calculation, the prediction model is used to improve the accuracy of the recommendation algorithm. Experimental results on Moivelens dataset show that the IP-CF algorithm significantly improves the accuracy of the recommended results, and the RMSE and MAE evaluation results are better than the traditional algorithms.

Improvement of Collaborative Filtering Recommendation Algorithm Based on Intuitionistic Fuzzy Reasoning Under Missing Data

“…In the collaborative filtering process, users' preferences should be collected first, then similar users should be found, and items finally should be recommended to the target user. When searching for similar users, the Pearson correlation coefficient is generally used to calculate the similarity [21] as Equation 1.…”