Learning collection fusion strategies
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Experiences with selecting search engines using metasearch
ACM Transactions on Information Systems (TOIS)
Analyses of multiple evidence combination
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Database merging strategy based on logistic regression
Information Processing and Management: an International Journal
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Modeling score distributions for combining the outputs of search engines
Proceedings of the 24th 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
Building efficient and effective metasearch engines
ACM Computing Surveys (CSUR)
Fusion Via a Linear Combination of Scores
Information Retrieval
Result merging strategies for a current news metasearcher
Information Processing and Management: an International Journal
Adaptive combination of evidence for information retrieval
Adaptive combination of evidence for information retrieval
A semisupervised learning method to merge search engine results
ACM Transactions on Information Systems (TOIS)
Retrieval evaluation with incomplete information
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Random sampling from a search engine's index
Proceedings of the 15th international conference on World Wide Web
ProbFuse: a probabilistic approach to data fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A study of results overlap and uniqueness among major web search engines
Information Processing and Management: an International Journal
Towards personalized distributed information retrieval
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Enhancing Web Search by Aggregating Results of Related Web Queries
WISE '09 Proceedings of the 10th International Conference on Web Information Systems Engineering
Extending probabilistic data fusion using sliding windows
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Estimating probabilities for effective data fusion
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Effective rank aggregation for metasearching
Journal of Systems and Software
Foundations and Trends in Information Retrieval
Cluster-based fusion of retrieved lists
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
The linear combination data fusion method in information retrieval
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
Re-ranking search results using an additional retrieved list
Information Retrieval
From "identical" to "similar": fusing retrieved lists based on inter-document similarities
Journal of Artificial Intelligence Research
Query sampling for learning data fusion
Proceedings of the 20th ACM international conference on Information and knowledge management
Linear combination of component results in information retrieval
Data & Knowledge Engineering
Predicting query performance for fusion-based retrieval
Proceedings of the 21st ACM international conference on Information and knowledge management
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Metasearch and data-fusion techniques combine the rank lists of multiple document retrieval systems with the aim of improving search coverage and precision. We propose a new fusion method that partitions the rank lists of document retrieval systems into chunks. The size of chunks grows exponentially in the rank list. Using a small number of training queries, the probabilities of relevance of documents in different chunks are approximated for each search system. The estimated probabilities and normalized document scores are used to compute the final document ranks in the merged list. We show that our proposed method produces higher average precision values than previous systems across a range of testbeds.