A Collaborative Filtering Recommendation Algorithm-based on User Attribute and Rating

Yu-liang SHI, Jian ZHENG


Collaborative Filtering recommendation is one of the most widely used recommendation systems in e-commerce recommendation systems. However, because of the growing number of users and goods, the recommended quality of traditional collaborative filtering technology is getting lower and lower. In this paper, we propose a method of Similarity Measurement Based on User - Attribute and User - Score to improve the accuracy of similarity calculation between users. First, this method calculates the similarity of user attribute and the similarity of user rating. Next, we combine the two similarities into the new similarity measure based on weighted fusion. Finally, we incorporate this similarity measure into the traditional collaborative filtering algorithm and improve the recommended quality. The experimental results show that the presented algorithm can improve the recommendation accuracy and produce a better recommendation results.


Recommendation algorithm, Collaborative Filtering, User similarity, Improvement of similarity


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