基于DIKW图谱的虚拟社区用户性格分类与转换方法

随着社交网络的发展,网络虚拟社区的成员数量快速增长.在虚拟社区中,用户会偏向选择浏览自己喜欢的内容,同时倾向与具有相近或相关兴趣爱好或目的的人进行内容交流与处理合作.在虚拟社区中,用户之间的交互内容以数据、信息和知识的形态存在.虚拟社区上大量的数据、信息与知识形态的网络类型化资源中保留了许多网络用户使用"痕迹".这些痕迹是真实用户的数字化存在代表.为了实现对虚拟社群用户生成内容按照偏好和兴趣进行量化调控,采用数据信息知识及智慧(data information knowledge wisdom, DIKW)图谱对这些类型资源进行建模.根据用户DIKW图谱结合自我构建理论将用户按性格指数进行进一步的归类,并根据DIKW图谱结合自我决定理论将用户的心理需求分类.根据性格指数和心理需求设计了针对不同用户的不同的性格转换方法,模拟了用户生成内容的产生.

Personality Classification and Conversion Method of Virtual Community Personnel Based on DIKW Graph

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.