Content Similarity Matching Mechanism Based on Category for Hybrid P2P System

For the last few years, there has been a large volume of research on peer-to-peer (P2P) system, resulting in many hybrid P2P models. This model uses name-based matching method as a SP finds the contents in directory server. The existing SP based P2P information retrieval method simply depends on queries of requester and content´s name shared by peers, however, there is a limit to provide exact matching between query and content´s name due to the inaccuracy in the content´s name or keyword in the request. In this study, we propose an ISP2P (intelligent super-peer based P2P system) using presented category-based content similarity matching algorithm. The proposed system can solve the problem incurred due to the discrepancy between names of contents. Also peers in the network can choose the peer with high relevance value based on category.