Abstract: To address the difficulty of data sparsity and lower recommendation precision in the traditional collaborative filtering recommendation algorithm,a new collaborative filtering recommendation method was presented based on dynamic social behavior and users’ background information.As the result of user annotation behavior,variable social tags can reflect the changes of user social behavior.Firstly,the similarities of users’ dynamic preferences are calculated based on users’ social tags.Secondly,the similarities between users are calculated based on users’ background information.Finally,the similarities of user rating are calculated based on time weight,and the above three similarities are integrated to get the nearest neighbor set for targeted users to provide more accurate individual recommendation.The experimental results show that the new method can not only improve the accuracy of recommendation,but also solve the problems of data sparsity and cold-start.

Key words: Recommendation precision,Cold-start,Social tags,Users’ background information,Dynamic social beha-vior,Time weight