Three-way recommendation for a node and a community on social networks
Tóm tắt
Compared to the traditional two-way decision making, three-way decision model with a third pending decision has been widely applied since it was put forward. Now it has become a methodology for solving problems in many fields. In social networks, recommending each other between a node and a community has attracted lots of attention. In this paper, we make three-way recommendation for a node and a community, which leads to three results: recommended, not recommended, and pending. Firstly, we divide the network into attribute information table and network structure, and analyze several forces that may affect the relationship between nodes and communities. On the attribute information table, we define the first force based on equivalence relation and conditional probability. In the network structure, we apply the double bounded rough set model of a pair of nodes to a pair of node and community to define other forces. To construct three-way recommendation model, we use the Logistic Regression algorithm to determine the weights of the forces and the evaluation function. Next, inspired by the threshold calculation algorithm based on the maximum weighted entropy, we propose a supervised one. We tested the model on five real networks, and the methods of determining evaluation function and threshold pair are compared with the existing works. Moreover, compared with the existing node-community recommendation models, the rationality and feasibility of the model are shown.
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