Non Negative Multiple Matrix Factorization with Social Similarity for Recommender Systems
A key problem in online social networks is the identification of users' link information and the analysis of how these are reflected in the recommender systems. The basis to tackle this issue is user similarity measures. In this paper, we propose non negative multiple matrix factorization with social similarity for recommender systems, considering the similarities between users, the relationships of users resources and tags resources. On this basis, we comparatively analyzed different performances of the recommendation with every similarity measure between users. In addition, our method can also recommend friends, resources, and tags to users. Experimental results on Lastfm and Delicious datasets show that the proposed method can significantly improve the recommendation accuracy compared with the art collaborative filtering methods.
2016 IEEE/ACM 3rd International Conference on Big Data Computing Applications and Technologies (BDCAT)