The Research Of Service Recommendation In Social Network Service Under Mass Behavior

The Internet has changed the traditional literature resource utilization pattern, has alsopromoved interaction between people, and promoted exchanges of resources, bringing theemergence of service platform for network public users accessing resources.Netizens are bothusers of the site content and the producer of the website content in Web2.0era, the behaviorof Netizens accessing same resources can be seen as mass behavior. Internet services based onmass behavior developes rapidly, which have been incorporated into life of the majority ofNetizens. From the perspective of mass behavior theory in sociology, six forms of Internetmass behavior are analyzed according to the system platform characteristics,that’s SocialNetwork Service,Blog, Public comment,Wiki, BBS and MicroBlog. The social networkstructure is classed in No to the social network, Have to social network and social networkconstructed by information intermediary.Along with the prevalence of social network service, interpersonal relationships networkand virtual social network repeatedly, which make the method of network mining method toget user’s social network become feasible. Take public service website Douban(www.douban.com) as experimental data sources, behaviour of accessing resources online ismass behavior, and the relationship between users is social network. This is paper focused onuser service recommendation on the basis of user interest and its social network. User interestcould be analyzed from friends, access behavior and group joined, and the informationcommunication mechanism in mass behavior is formed by the user, resource and group.Labels of resource accessed by user reflect its interst. Resources are recommended to useraccording to its labels and user interst. Groups are recommended to user according to groupsof uers who are in the groups which the user joined. In addition, there is correlation betweentheme of group and the content of resource. Related groups could be recommended to the userif it’s interested in the resources. Research on the impact of user interst to social network, andthen recommend similar interst user, interested resources and interested groups to the user,which could improve service quality of social network service.