Collaborative Filtering Algorithm Based on Rating Prediction and User Characteristics

Abstract

Collaborative filtering directly predicts potential favorite items of user based on user's behavior records. It is one of the key technologies in personalized recommendation systems. The traditional similarity measurement method relies on user's rating data in the case of data sparseness, which causes a decrease in the recommendation quality of recommendation systems. To solve this problem, this paper proposes a collaborative filtering algorithm based on item rating prediction and user characteristics. The first step is to select the k nearest neighbor sets of the item using the KNN algorithm, and then calculate the similarity between the items using the improved similarity measurement method, and initially predict the user's rating on the unrated item to improve the sparsity problem. The second step considers the user characteristics when predicting the similarity between users according to the item ratings. Finally, the algorithm combining item-based rating prediction and user characteristics is adopted to make recommendations for the user. The experimental results on MovieLens and Douban datasets show that the proposed collaborative filtering algorithm based on rating prediction and user characteristics can effectively improve the quality of recommendation system compared with the traditional algorithm.