Analyses of multiple evidence combination
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
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
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Relevance score normalization for metasearch
Proceedings of the tenth international conference on Information and knowledge management
Comparison of Normalization Techniques for Metasearch
ADVIS '02 Proceedings of the Second International Conference on Advances in Information Systems
Relevance weighting for query independent evidence
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information 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
A support vector method for optimizing average precision
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
AdaRank: a boosting algorithm for information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A formal approach to score normalization for meta-search
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Integrating Proximity to Subjective Sentences for Blog Opinion Retrieval
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
A signal-to-noise approach to score normalization
Proceedings of the 18th ACM conference on Information and knowledge management
Proximity-based opinion retrieval
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Quality-biased ranking of web documents
Proceedings of the fourth ACM international conference on Web search and data mining
Modeling score distributions in information retrieval
Information Retrieval
Copulas for information retrieval
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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In many Information Retrieval (IR) tasks, documents should be ranked based on a combination of multiple criteria. Therefore, we would need to score a document in each criterion aspect of relevance and then combine the criteria scores to generate a final score for each document. Linear combination of these aspect scores has so far been the dominant approach due to its simplicity and effectiveness. However, such a strategy of combination requires that the scores to be combined are "comparable" to each other, an assumption that generally does not hold due to the different ways of scoring each criterion. Thus it is necessary to transform the raw scores for different criteria appropriately to make them more comparable before combination. In this paper we propose a new principled approach to score transformation in linear combination, in which we would learn a separate non-linear transformation function for each relevance criterion based on the Alternating Conditional Expectation (ACE) algorithm and BoxCox Transformation. Experimental results show that the proposed method is effective and is also robust against non-linear perturbations of the original scores.