Syncretizing Context Information into the Collaborative Filtering Recommendation

Social network allows users to organize collections of resources on the web in a collaborative fashion. Collaborative filtering as a classical method has been also used in helping people to deal with information overload in folksonomy system. The problem of devising methods to solve the contextual problems emerging in the process of recommendation application over the social network is increasing open. Here we propose a novel means to syncretize context information into the recommender system. This paper first recall traditional methods of collaborative filtering, then presents some definitions and algorithm framework, proposes a contextual rating estimation. Finally, experiment comparison demonstrates that the contextual approach can produces better rating estimations.