Collaborative Filtering Algorithm based on Data Mixing and Filtering

摘要

Personalized recommendation systems based on the collaborative filtering algorithm are faced with an excessive user rating data sparseness problem. In order to solve this problem, an improved collaborative filtering algorithm is proposed, which gathers a variety of single numerical filling methods and selects a more appropriate filling method according to the filling rules to fill the vacant positions in the user-item scoring matrix filling. The recommendations are then made on the populated user-item score matrix through a user-based collaborative filtering approach. The method of data mixed filling can effectively reduce the recommended error and numerical singularity caused by fixed filling values such as the mean and median. The improved collaborative filtering algorithm is tested on the Movie Lens data set. The results show that the method of data mixing is adopted to fill the empty positions in the scoring matrix, which effectively alleviates the data sparsily problem in the collaborative filtering algorithm and improves the accuracy of recommendation systems for target users.