Incorporating Social Tagging for Unified Probabilistic Matrix Factorization Recommendation

With the development of social network, the information in social network environment is increasing rapidly. It is a hot issue that how to recommend the resources to users according to their interests using big data. In order to improve the quality of recommendation, this paper proposes a unified probability matrix factorization recommendation model called TSRec which integrates social tags. The model uses the information of users, resources and tags of social network, and optimizes the parameters of the training model to provide the best recommendation effect.