Ranked Accuracy and Unstructured Distributed Search

Non-uniformly distributing documents in an unstructured peer-to-peer (P2P) network has been shown to improve both the expected search length and search accuracy, where accuracy is defined as the size of the intersection of the documents retrieved by a constrained, probabilistic search and the documents that would have been retrieved by an exhaustive search, normalized by the size of the latter. However neither metric considers the relative ranking of the documents in the retrieved sets. We therefore introduce a new performance metric, rank-accuracy, that is a rank weighted score of the top-k documents retrieved. By replicating documents across nodes based on their retrieval rate (a function of query frequency), and rank, we show that average rank-accuracy can be improved. The practical performance of rank-aware search is demonstrated using a simulated network of 10,000 nodes and queries drawn from a Yahoo! web search log.