Ranking search results in peer-to-peer systems

P2P deployments are a natural infrastructure for building distributed search networks. Proposed systems support locating and retrieving all results, but lack the information necessary to rank them. Users, however, are primarily interested in the most relevant, and not all possible results. Using random sampling, we extend a class of well-known information retrieval ranking algorithms such that they can be applied in this distributed setting. We analyze the overhead of our approach, and quantify exactly how our system scales with increasing number of documents, system size, document to node mapping (uniform versus non-uniform), and types of queries (rare versus popular terms). Our analysis and simulations show that a) these extensions are efficient, and can scale with little overhead to large systems, and b) the accuracy of the results obtained using distributed ranking is comparable to a centralized implementation.