Personalized Recommendation Method For Social Network

With the continuous development of service-oriented computing, there are always constantly emerging new service that having same functional properties with each other. The key technology of Web Services Recommendation is how to rapidly choose services from the candidates that meet the demands for target users.The current social networks such as Facebook, Twitter and location-based social networking(LBSN), have been widely used with the increasing number of users. How to quickly retrieve service information in line with their own interests and preferences has become a topic of concern for users. Despite the traditional method of personalized recommendation has a good performance, however, due to the social network’s own characteristics, the accuracy of these traditional methods are greatly reduced. This article focuses on the association of social network users, the interest transfer,and the affection of the attributes of project itself. This paper proposed a method that use data and users’ relations between LBSN to generate a list of recommended, using users’ photos of scenic spot to assess their preferences; using intimacy and user rating data for scenic spots to meet the similarity uses. Taking into account the interest of the users’ preferences with the suitable travel time of recommended location as well as other suitable areas nearby the candidates when making recommendation.Existing studies using SNS trust usually only consider the relationship between the user community or service in a particular aspect of the overall score. These methods had some drawbacks. When selecting services from a vast amount of services, users often choose services with high composite score and good reputation meanwhile, users will also refer their friends’ comments. This article was explored that how to choose services from lots of services with similar functions to meet the requirements is a question. We have to take into account the popularity of the global score when the target user and his friends had not selected a target service. However when a user used to select the service, we also adopt the local trust with his friends’ comments, then combined the strategy to form the recommended list. Finally, we used experiments to verify the validity of the proposed methods.