Study Of Semantic Recommender Based On Social-tagging In P2P Networks

Due to the distribution of data, unstructured and lack of semantic expression and description in P2P environment, it leads to the low retrieval efficiency, search cost also rapid increase with the network information increased. For this problem, this paper introduced the social-tagging, according to the node user on resources to add tags gets its interest to active recommendation information resources, for the study of distributed information sharing bring new enlightenment.Because of the defect of retrieving information with keywords matching in P2P system, the social-tagging is introduced in this paper and the semantic recommendation model of social-tagging-based was built. First, the hierarchical relationships between tags should be revealed in P2P community tag ontology which is built according to the tag and category input by P2P node users. Then,the semantic related tags or sources to users’ history tag sets are recommended through matching users’ history tag sets with community tag ontology to realize the semantic recommendation, Finally, an example is given to validate the efficiency of the proposed method. hen, how to accurately describe the user interest is the key of realization personalized recommendation. For semantic recommender based on Social-tagging in P2P networks, only used the user’history tag sets as the user’ interest tag that don’t find user’ potential interest tag. This paper depend on friends may have same interest or hobby and we use community friends’ interest tag sets enhanced active users’ history tag sets, mining the potential interest and the improved P2P semantic recommendation model was built. Finally, the potential tags or sources are recommended to active user and an example is revealed to validate the efficiency of the proposed method..