Collaborative Filtering Recommendation Method Based On Online Ratings And Social Tags

The rapid development of the social economy and the E-commerce brings a lot of convenience to the consumers,and also brings the problem of the "information overload".Collaborative filtering is one of the most advanced methods in the recommendation system,but there are still data sparsity,extendibility and cold start.In the real life,people prefer to accept the trust relationship information,and can further improve the user acceptance.However,that explicit trust information is difficult to obtain for privacy,and the data is sparse due to the large number of users.Therefore,how to help users to make product recommendations based on online ratings and social tags is a subject of concern and of practical significance.In this paper,the collaborative filtering recommendation method based on online ratings and social tags mainly completed in the following four aspects of research work.(1)The research framework of collaborative filtering recommendation method based on online ratings and social tags is given.In particular,the description and research framework of collaborative filtering recommendation problem based on implicit trust network is given.(2)An implicit trust network based on online ratings and social tags is proposed.In this paper,the user similarity based on online ratings is calculated from three dimensions;then the user similarity based on social tags is calculated;finally,the comprehensive similarity based on online ratings and social tags is calculated,and the implicit trust network is constructed.(3)A collaborative filtering recommendation method based on implicit trust network and Pareto dominance is proposed.In this paper,the confidence of the initial prediction ratings are calculated,then the representative neighbor user set is found based on Pareto dominance theory,and finally the final prediction score is calculated based on time attenuation.(4)The application research of movie recommendation based on MovieLens website is given.The research results of this paper are applied to the film recommendation problem based on MovieLens website,which explains the feasibility and applicability of the method proposed in this paper.The collaborative filtering recommendation method based on online ratings and social tags can be used for solving the problem that the consumer can not quickly and accurately make the purchase decision because of the information overload problem in the reality,This paper lays a foundation for the research of the proposed method based on the implicit trust network.