To improve the performance of similarity search and information retrieval is an important research issue in peer-to-peer environment. In this paper, we propose a distributed architecture for enhancing the performance of similarity search in unstructured P2P networks. The key component of the proposed architecture is a distributed, content-based, heuristic feedback mechanism, which allows peers to keep track of recent queries and learn from the assessment of answers to previous queries, so as to self-adaptively route the subsequent query to the most relevant nodes which are responsible for the query. Therefore a high recall rate can be achieved by probing only a small amount of peers. We also propose a distributed automatic query expansion mechanism to improve the quality of query results. Since the architecture is entirely distributed, it scales well with the large sized networks. The experimental results show that our architecture can efficiently solve queries with a relatively small cost.
['Xiaoyu Yang', 'Yiming Hu']
SEIF: Search Enhanced by Intelligent Feedback in Unstructured P2P Networks