Searching distributed collections with inference networks
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
SDLIP + STARTS = SDARTS a protocol and toolkit for metasearching
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
Proceedings of the 11th international conference on World Wide Web
ACM Transactions on Internet Technology (TOIT)
Efficient and decentralized PageRank approximation in a peer-to-peer web search network
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Computing pagerank in a distributed internet search system
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Beagle++: semantically enhanced searching and ranking on the desktop
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Peer-Sensitive objectrank – valuing contextual information in social networks
WISE'05 Proceedings of the 6th international conference on Web Information Systems Engineering
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In distributed work environments, where users are sharing and searching resources, ensuring an appropriate ranking at remote peers is a key problem. While this issue has been investigated for federated libraries, where the exchange of collection specific information suffices to enable homogeneous TFxIDF rankings across the participating collections, no solutions are known for PageRank-based ranking schemes, important for personalized retrieval on the desktop. Connected users share fulltext resources and metadata expressing information about them and connecting them. Based on which information is shared or private, we propose several algorithms for computing personalized PageRank-based rankings for these connected peers. We discuss which information is needed for the ranking computation and how Page-Rank values can be estimated in case of incomplete information. We analyze the performance of our algorithms through a set of experiments, and conclude with suggestions for choosing among these algorithms.