Abstract
A metasearch engine is an Information Retrieval (IR) system that can query multiple search engines and aggregate ranked list of results returned by them into a single result list of documents, ranked in descending order of relevance to a query. The result aggregation problem have been largely treated as Multi-Criteria Decision Making (MCDM) problem with previous approaches applying simple MCDM techniques such as average, sum, weighted average, weighted sum. Previous research has demonstrated the effectiveness of applying Yager’s Fuzzy Ordered Weighted Average (OWA) operator and its variants in result aggregation. In this paper we propose a result aggregation model based on the Choquet Integral, called Choquet Capacity-Guided Aggregation (CCGA) model which represents an alternative way to aggregate results for metasearch using most equilibrated conditions. We compare the proposed model against existing result aggregation models such as the Borda-Fuse, Weighted Borda-Fuse, OWA and IGOWA.
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De, A., Diaz, E.D., Raghavan, V.V. (2012). Weighted Fuzzy Aggregation for Metasearch: An Application of Choquet Integral. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances on Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31709-5_51
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DOI: https://doi.org/10.1007/978-3-642-31709-5_51
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