An recommendation algorithm based on weighted Slope one algorithm and user-based collaborative filtering

Abstract

Personalized recommendation is one of the most popular marketing methods, and collaborative filtering is one of the most successful recommendation technologies. However, data sparsity problem results in the low prediction accuracy and the poor recommendation quality. To resolve this problem, the present study proposed an improved recommendation method with weighted Slope one algorithm. The method calculates the similarity between users based on users' ratings, so as to find every user's nearest neighbors. Based on the nearest neighbor's ratings, weighted Slope one algorithm is used to predict the unknown ratings of the target user and to present recommendation results. In the experiment, MovieLens data set was used to test the recommendation accuracy of the method. The experimental results suggest that the improved algorithm can effectively improve the accuracy of rating prediction and the recommendation performance.