Search (2 results, page 1 of 1)

0.09734263 = product of:
  0.19468527 = sum of:
    0.089426495 = weight(_text_:engines in 2554) [ClassicSimilarity], result of:
      0.089426495 = score(doc=2554,freq=2.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.39294976 = fieldWeight in 2554, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2554)
    0.10525877 = product of:
      0.21051754 = sum of:
        0.21051754 = weight(_text_:programming in 2554) [ClassicSimilarity], result of:
          0.21051754 = score(doc=2554,freq=4.0), product of:
            0.29361802 = queryWeight, product of:
              6.5552235 = idf(docFreq=170, maxDocs=44218)
              0.04479146 = queryNorm
            0.7169776 = fieldWeight in 2554, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              6.5552235 = idf(docFreq=170, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2554)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Ranking functions play a substantial role in the performance of information retrieval (IR) systems and search engines. Although there are many ranking functions available in the IR literature, various empirical evaluation studies show that ranking functions do not perform consistently well across different contexts (queries, collections, users). Moreover, it is often difficult and very expensive for human beings to design optimal ranking functions that work well in all these contexts. In this paper, we propose a novel ranking function discovery framework based on Genetic Programming and show through various experiments how this new framework helps automate the ranking function design/discovery process.

0.054213837 = product of:
  0.21685535 = sum of:
    0.21685535 = sum of:
      0.1804436 = weight(_text_:programming in 2239) [ClassicSimilarity], result of:
        0.1804436 = score(doc=2239,freq=4.0), product of:
          0.29361802 = queryWeight, product of:
            6.5552235 = idf(docFreq=170, maxDocs=44218)
            0.04479146 = queryNorm
          0.6145522 = fieldWeight in 2239, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            6.5552235 = idf(docFreq=170, maxDocs=44218)
            0.046875 = fieldNorm(doc=2239)
      0.036411747 = weight(_text_:22 in 2239) [ClassicSimilarity], result of:
        0.036411747 = score(doc=2239,freq=2.0), product of:
          0.15685207 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.04479146 = queryNorm
          0.23214069 = fieldWeight in 2239, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046875 = fieldNorm(doc=2239)
  0.25 = coord(1/4)

Abstract

Genetic-based evolutionary learning algorithms, such as genetic algorithms (GAs) and genetic programming (GP), have been applied to information retrieval (IR) since the 1980s. Recently, GP has been applied to a new IR taskdiscovery of ranking functions for Web search-and has achieved very promising results. However, in our prior research, only one fitness function has been used for GP-based learning. It is unclear how other fitness functions may affect ranking function discovery for Web search, especially since it is weIl known that choosing a proper fitness function is very important for the effectiveness and efficiency of evolutionary algorithms. In this article, we report our experience in contrasting different fitness function designs an GP-based learning using a very large Web corpus. Our results indicate that the design of fitness functions is instrumental in performance improvement. We also give recommendations an the design of fitness functions for genetic-based information retrieval experiments.

Date

31. 5.2004 19:22:06