Research on dynamic recommendation trust based on GC-TOPSIS in grid

Abstract

In order to effectively reduce the impact of malicious recommendation, this paper designs a method of dynamic recommendation trust based on the gray correlation technique for order preference by similarity to ideal solution (GCTOPSIS). First, we determine the combined weights of each node attribute by using AHP and entropy method. Next, we use the gray correlation analysis to calculate the gray correlation relative closeness degree (GCRCD) of the recommendation nodes and the reference nodes, and sort all the recommendation nodes according to the GCRCD. Finally, according to the importance of the interaction between the nodes, we can dynamically select the recommendation nodes and obtain the set of the trusted recommendation nodes. The experiment shows the method can filter out the malicious nodes, thus effectively reducing the impact of malicious recommendation.