Abstract
This paper proposes a collaborative filtering algorithm based on user characteristics. The algorithm is mainly aimed at the problems of data sparsity and cold start in traditional collaborative filtering algorithm. First, through the analysis of user behavior, the time-interest weight function is introduced to improve the modified cosine similarity formula; then both the user preference degree and trust degree are introduced to improve the accuracy of the recommendation results. Finally, the experimental results based on hetrec2011 dataset show that the accuracy of the recommendation generated by the improved collaborative filtering algorithm is significantly improved compared with the traditional recommendation algorithm. The problems of data sparsity and cold start are effectively alleviated.
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