Research On Trust Model Based Recommendation In P2P Networks

With the development of information technology, P2P has become the overwhelming technology for the internet as its advantage in cooperative working, data sharing and scale parallel computing. P2P network adopts a distributing network structure and every node is responsible of the client role and the server. However, the nature of openness, anonymity and loose-coupling relationship among peers in P2P systems make it an ideal environment for malicious users to spread unsolicited and harmful content, opening the door to abuses of the networks, so it is necessary to build up a rational trust model in P2P network.The recommendation trust models of P2P systems are investigated in this thesis.Based on the analysis of traditional recommended trust models and the characteristics of social relationships in real world, we presents a Peer-to-Peer trust model-IPBS, which introduces content similarity and recommendation feedback to calculate the peer's recommended values. The recommendation ability of nodes is evaluated according to content similarity between peers. The similarity varies with the different transaction. Experimental results demonstrate that the trust model has advantages of dynamic adaptability and is highly effective in the search efficiency.This paper also proposes a RBTM based on time series relation to recommendation trust. Time series Vari-Segment Dimension Reduction is applied to evaluate time unit in model. Some reasonable time degenerating factors and a detailed description to the addition of a new data are introduced. According to the historical time information of nodes transaction, it can adaptively update the recommendation trust based on content similarity and recommendation feedback. Experimental results shows that the trust model has better flexibility and lower operating expenses.