PIST: A P2P IR Model for Social-Tagging Network
Wang Zhenhua, Shen Derong, Yu Ge · 2009
This paper proposes a P2P information retrieval model for social-tagging network (PIST), which is layered on top of a DHT-based overlay network. The tags of document, user's friends, interests and feedback are exploited to improve the search performance. With the help of friends, the user can get his desired documents with one-hop access. A document is associated with a document profile consisting of tags which describes the document. A user is associated with user profiles which represent his interests. During query processing, the relevant results can be returned according to user's interest, and user's feedback is exploited to refine the profiles and friends list. Experiments show that PIST has good performance and it is suitable for social-tagging network.