A Hybrid Recommendation Model for Community Attributes of Social Networks Based on Association Rule Mining

Abstract

In order to improve the object-oriented nature of social network services and customize the personalized social network service system, it needs to design a hybrid social network community attribute recommendation model. A hybrid recommendation model for social network community attributes is proposed based on association rule mining. Based on the distributed attribute features of the community in the social network, the association rules data mining is carried out, and the information transfer model of the social network is constructed based on user behavior, community attribute and scene information. The association rules characteristic quantity of social network community attribute is mined, fuzzy directivity constraint control method is adopted to carry on the social network community attribute characteristic clustering processing, the fuzzy C mean algorithm is used to fuse the characteristic quantity of the cluster output. The ability to discover community attributes is improved. According to the result of community attribute mining of social network, the mixed recommendation of social group is carried out, and the demand preference of users is obtained, and the hybrid recommendation algorithm of community attribute of social network based on association rule mining is improved. The simulation results show that the proposed method has better accuracy, higher degree of personalized matching, and higher confidence level of community attributes, which improves the community's discovery ability.