Exploring Social User Behavior with Personalized Recommendation

The advent and popularity of social network, more and more users like to share their experiences, such as ratings, reviews, and blogs. The new factors of social network like interpersonal influence and interest based on circles of friends bring opportunities and challenges for recommender system (RS) to solve the cold start and sparsity problem of datasets. Some of the social factors have been used in RS, but have not been fully considered. At present the personalized recommendation model only takes the user historical rating records. To propose a -Aware Service Recommendation method KASR, to sole the existing system challenges. It aims at presenting a personalized service recommendation list and recommending the most appropriate services to the users effectively. s are used to indicate users preferences and a user based collaborative Filtering method is used to generate the approprirate recommendations. Here use the location of user information to recommend personalizing. The KASR significantly improves the accuracy of service recommender system.