Collaborative Filtering Recommendation Methods In Social Commerce Environment

The social applications of Web2.0 have made great changes in Internet life,and their business potential is constantly being released under the efforts of e-commerce companies.Social commerce has become the trend of the development of e-commerce.In the social commerce environment,the trust relationship has a profound impact on the consumer's purchase decision,and it has become an important factor to support the development of social commerce activities.Today,the social commerce environment has a wealth of data resources,and these data provide the basis for the research on trust relationship.Therefore,in order to improve the accuracy of the traditional recommendation methods,this thesis will fully explore the social and project scoring data,and study the trust relationship between users.Finally,we'll find a reasonable way to integrate trust into the recommended method.Based on social data,this paper proposes a collaborative filtering recommendation method based on social relationship and trust propagation.Firstly,in order to make social data to express social trust more credible,this reserach will use a series of social attribute data to calculate the user's relability rating.In order to improve the trust level of social trust and improve the density of the rating matrix,relability score will be used to quantify the credibility of the social matrix and prefill rating matrix.In order to express the social trust relationship more sufficient,this reserach considers the direct,indirect and co-citation trust relationship,which will help to accurately find trust neighbors and calculate the similarity.Finally,this reserach proposes a method of similarity calculation based on trust propagation,which makes the similarity calculation more reasonable and effective.Experiments on Epinions and Dianping data sets,and the experimental results show that the proposed method has excellent performance in MAE,Precision,Recall and F-Measure and outperform the existing related methods.In the social commerce environment,the user's consensus on the item rating reflects the implicit trust relationship between users.Based on item rating data,this paper proposes a collaborative filtering recommendation algorithm based on item rating and trust mining.In the process of constructing implicit trust,most of the research was not only considered inadequate on user characteristics,but also didn't take into account the trust transfer characteristics.What's more,most of the research only reserach the implicit trust recommendation based on the user.To our knowledge,no literature reports the research on the trust relationship between users and items.Therefore,this research will be more sufficiently consider the user's characteristics and trust transfer mechanism in the item rating,and will be based on the user and the item perspective to mining the implicit trust relationship between users.Finally,we propose a comprehensive implicit trust recommendation method which combines the two methods.Experiments on Movielens and Dianping data sets,and the experimental results indicate that the proposed recommendation method provide better performance compared with the traditional recommendation methods,and can provide users with more accurate recommendations.