On CORI Results Merging

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.