RankDE: learning a ranking function for information retrieval using differential evolution

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@InProceedings{Bollegala:2011:GECCO,
  author =       "Danushka Bollegala and Nasimul Noman and Hitoshi Iba",
  title =        "{RankDE:} learning a ranking function for information
                 retrieval using differential evolution",
  booktitle =    "GECCO '11: Proceedings of the 13th annual conference
                 on Genetic and evolutionary computation",
  year =         "2011",
  editor =       "Natalio Krasnogor and Pier Luca Lanzi and 
                 Andries Engelbrecht and David Pelta and Carlos Gershenson and 
                 Giovanni Squillero and Alex Freitas and 
                 Marylyn Ritchie and Mike Preuss and Christian Gagne and 
                 Yew Soon Ong and Guenther Raidl and Marcus Gallager and 
                 Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and 
                 Nikolaus Hansen and Silja Meyer-Nieberg and 
                 Jim Smith and Gus Eiben and Ester Bernado-Mansilla and 
                 Will Browne and Lee Spector and Tina Yu and Jeff Clune and 
                 Greg Hornby and Man-Leung Wong and Pierre Collet and 
                 Steve Gustafson and Jean-Paul Watson and 
                 Moshe Sipper and Simon Poulding and Gabriela Ochoa and 
                 Marc Schoenauer and Carsten Witt and Anne Auger",
  isbn13 =       "978-1-4503-0557-0",
  pages =        "1771--1778",
  keywords =     "genetic algorithms, genetic programming, Real world
                 applications",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001576.2001814",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Learning a ranking function is important for numerous
                 tasks such as information retrieval (IR), question
                 answering, and product recommendation. For example, in
                 information retrieval, a Web search engine is required
                 to rank and return a set of documents relevant to a
                 query issued by a user. We propose RankDE, a ranking
                 method that uses differential evolution (DE) to learn a
                 ranking function to rank a list of documents retrieved
                 by a Web search engine. To the best of our knowledge,
                 the proposed method is the first DE-based approach to
                 learn a ranking function for IR. We evaluate the
                 proposed method using LETOR dataset, a standard
                 benchmark dataset for training and evaluating ranking
                 functions for IR. In our experiments, the proposed
                 method significantly outperforms previously proposed
                 rank learning methods that use evolutionary computation
                 algorithms such as Particle Swam Optimization (PSO) and
                 Genetic Programming (GP), achieving a statistically
                 significant mean average precision (MAP) of 0.339 on
                 TD2003 dataset and 0.430 on the TD2004 dataset.
                 Moreover, the proposed method shows comparable results
                 to the state-of-the-art non-evolutionary computational
                 approaches on this benchmark dataset. We analyze the
                 feature weights learnt by the proposed method to better
                 understand the salient features for the task of
                 learning to rank for information retrieval.",
  notes =        "Also known as \cite{2001814} GECCO-2011 A joint
                 meeting of the twentieth international conference on
                 genetic algorithms (ICGA-2011) and the sixteenth annual
                 genetic programming conference (GP-2011)",
}

Genetic Programming entries for Danushka Bollegala Nasimul Noman Hitoshi Iba

Citations