With the development of social networks, the members of online virtual communities have grown rapidly. In virtual communities, users generally prefer browsing the contents they like, and tend to communicate and cooperate with people with similar or related interests or purposes. The interactive contents between users exist in the form of data, information and knowledge, and generally retain rich “traces” of network users. These traces represent the digital presence of real users. In order to achieve quantitative control of user-generated content in virtual communities based on preferences and interests. This paper proposes to use the DIKW (data, information, knowledge, wisdom) graph to model these typed resources. Combining the user;s DIKW Graph with self-construction theory, users are further classified according to personality index, and the users; psychological needs are also classified. According to the personality index and psychological needs, appropriate personality conversion methods are designed for different users, and the generation of user-preference content is simulated.
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