An improved recommendation algorithm via social behaviors

Recommendation is an important problem in the traditional field of data mining. As a consequence, various kinds of algorithms have been proposed in the last few years to improve the recommendation performance. However, many of them overlook users' rating behaviors. In this paper, an improved recommendation algorithm with considering users' habits and rating behaviors will be proposed. Firstly, calculate the ratings entropy for each user via users' rating records which can reflect users' behavioral features. Secondly, widely-used user-based method will be improved by considering rating entropy. Finally, the predicting rate is generated. Experimental results on the three datasets: Movie Lens, Netflix and RYM all suggest that the proposed method can enhance the algorithmic accuracy. Furthermore, since this method can improve those missing value more accurately via users' similarity and rating behaviors which might shed some light on discovering users' purchasing intention and optimizing the performance of recommender systems by human dynamics.