Search (1 results, page 1 of 1)

  • × author_ss:"Baeza-Yates, R."
  • × theme_ss:"Data Mining"
  1. Baeza-Yates, R.; Hurtado, C.; Mendoza, M.: Improving search engines by query clustering (2007) 0.00
    0.003606434 = product of:
      0.014425736 = sum of:
        0.014425736 = weight(_text_:information in 601) [ClassicSimilarity], result of:
          0.014425736 = score(doc=601,freq=6.0), product of:
            0.06134496 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.034944877 = queryNorm
            0.23515764 = fieldWeight in 601, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0546875 = fieldNorm(doc=601)
      0.25 = coord(1/4)
    
    Abstract
    In this paper, we present a framework for clustering Web search engine queries whose aim is to identify groups of queries used to search for similar information on the Web. The framework is based on a novel term vector model of queries that integrates user selections and the content of selected documents extracted from the logs of a search engine. The query representation obtained allows us to treat query clustering similarly to standard document clustering. We study the application of the clustering framework to two problems: relevance ranking boosting and query recommendation. Finally, we evaluate with experiments the effectiveness of our approach.
    Footnote
    Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"
    Source
    Journal of the American Society for Information Science and Technology. 58(2007) no.12, S.1793-1804