Optimized CF Recommendation Algorithm Based on Users' Characteristics and Trust

Abstract

CF (Collaborative filtering) algorithm has the widest and most successful applications in personalized recommendations. However, due to its over-reliance on the users' historical data, it is difficult to avoid data sparseness and cold start issues. The data sparseness and cold start may cause poor recommendation accuracy of the collaborative filtering algorithm. A hybrid optimal collaborative filtering algorithm based on users' characteristics and trust is proposed in this paper. In the process of users' similarity calculation, the age and gender of users' characteristics are introduced to make the determination of nearest neighbor more accurate. Besides, in order to improve the recommendation accuracy of the traditional CF recommendation algorithm, the trust relationship is introduced into the prediction score by measuring the users' trust, and this improvement will be used in the recommendation of new items in order to improve the recommendation accuracy of the traditional CF recommendation algorithm. The experimental results of Movie lens data set show that the improved recommendation accuracy of the recommendation system can be achieved by the proposed algorithm. Also, the problems of cold start and sparse data can be solved effectively.