An evolutionary approach for combining different sources of evidence in search engines

  • Authors:

  • Thomaz Philippe C. Silva;Edleno Silva de Moura;João Marcos B. Cavalcanti;Altigran S. da Silva;Moisés Gomes de Carvalho;Marcos André Gonçalves

  • Affiliations:

  • Computer Science Department, Federal University of Amazonas, Manaus, Brazil;Computer Science Department, Federal University of Amazonas, Manaus, Brazil;Computer Science Department, Federal University of Amazonas, Manaus, Brazil;Computer Science Department, Federal University of Amazonas, Manaus, Brazil;Computer Science Department, Federal University of Minas Gerais, Belo Horizonte, Brazil;Computer Science Department, Federal University of Minas Gerais, Belo Horizonte, Brazil

  • Venue:
  • Information Systems
  • Year:
  • 2009

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Abstract

Modern Web search engines use different strategies to improve the overall quality of their document rankings. Usually the strategy adopted involves the combination of multiple sources of relevance into a single ranking. This work proposes the use of evolutionary techniques to derive good evidence combination functions using three different sources of evidence of relevance: the textual content of documents, the reputation of documents extracted from the connectivity information available in the processed collection and the anchor text concatenation. The combination functions discovered by our evolutionary strategies were tested using a collection containing 368 queries extracted from a real nation-wide search engine query log with over 12 million documents. The experiments performed indicate that our proposal is an effective and practical alternative for combining sources of evidence into a single ranking. We also show that different types of queries submitted to a search engine can require different combination functions and that our proposal is useful for coping with such differences.