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SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Searching distributed collections with inference networks
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
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SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
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Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Inquirus, the NECI meta search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Using sampled data and regression to merge search engine results
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Condorcet fusion for improved retrieval
Proceedings of the eleventh international conference on Information and knowledge management
Fusion Via a Linear Combination of Scores
Information Retrieval
STARTS: Stanford Protocol Proposal for Internet Retrieval and Search
STARTS: Stanford Protocol Proposal for Internet Retrieval and Search
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Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
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Journal of the American Society for Information Science and Technology
ProbFuse: a probabilistic approach to data fusion
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ECIR'07 Proceedings of the 29th European conference on IR research
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Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
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ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
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Proceedings of the 20th ACM international conference on Information and knowledge management
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Recent developments in the field of data fusion have seen a focus on techniques that use training queries to estimate the probability that various documents are relevant to a given query and use that information to assign scores to those documents on which they are subsequently ranked. This paper introduces SlideFuse, which builds on these techniques, introducing a sliding window in order to compensate for situations where little relevance information is available to aid in the estimation of probabilities. SlideFuse is shown to perform favourably in comparison with CombMNZ, ProbFuse and SegFuse. CombMNZ is the standard baseline technique against which data fusion algorithms are compared whereas ProbFuse and SegFuse represent the state-of-the-art for probabilistic data fusion methods.