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Learning Ranking Functions by Genetic Programming Revisited

  • Ricardo Baeza-Yates
  • Alfredo Cuzzocrea
  • Domenico Crea
  • Giovanni Lo Bianco
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11030)

Abstract

We revisit the use of Genetic Programming (GP) to learn ranking functions in the context of web documents, by adding linking information. Our results show that GP can cope with larger sets of features as well as bigger document collections, obtaining small improvements over the state-of-the-art of GP learned functions applied to web search.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Ricardo Baeza-Yates
    • 1
  • Alfredo Cuzzocrea
    • 2
    • 3
  • Domenico Crea
    • 4
  • Giovanni Lo Bianco
    • 4
  1. 1.Northeastern University at Silicon ValleySan JoseUSA
  2. 2.DIA DepartmentUniversity of TriesteTriesteItaly
  3. 3.ICAR-CNRRendeItaly
  4. 4.DIMES DepartmentUniversity of CalabriaRendeItaly

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