Collaborative filtering recommendation method of integrating social tags and users background information
Jiang Shen · Journal of Computer Applications · 2014
To address the difficulty of data sparsity and lower recommendation precision in the traditional Collaborative Filtering( CF) recommendation algorithm, a new CF recommendation method of integrating social tags and users background information was proposed in this paper. Firstly, the similarities of different social tags and different users background information were calculated respectively. Secondly, the similarities of different users ratings were calculated. Finally, these three similarities were integrated to generate the integrated similarity between users and undertook the recommendations about items for target users. The experimental results show that, compared with the traditional CF recommendation algorithm, the Mean Absolute Error( MAE) of the proposed algorithm respectively reduces by 16% and 22. 6% in the normal dataset and cold-start dataset. The new method can not only improve the accuracy of recommendation algorithm, but also solve the problems of data sparsity and cold-start.