Analyses of multiple evidence combination
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Relevant document distribution estimation method for resource selection
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Using historical data to enhance rank aggregation
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Robust result merging using sample-based score estimates
ACM Transactions on Information Systems (TOIS)
Classification-based resource selection
Proceedings of the 18th ACM conference on Information and knowledge management
Unsupervised linear score normalization revisited
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Distributed information retrieval and applications
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
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Score normalization and results merging are important components of many IR applications. Recently MinMax--an unsupervised linear score normalization method--was shown to perform quite well across various distributed retrieval testbeds, although based on strong assumptions. The CORI results merging method relaxes these assumptions to some extent and significantly improves the performance of MinMax. We parameterize CORI and evaluate its performance across a range of parameter settings. Experimental results on three distributed retrieval testbeds show that CORI significantly outperforms state-of-the-art results merging and score normalization methods when its parameter goes to infinity.