Applying layered multi-population genetic programming on learning to rank for information retrieval

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Jung Yi Lin and Jen-Yuan Yeh and Chao-Chung Liu",
  booktitle =    "International Conference on Machine Learning and
                 Cybernetics (ICMLC 2012)",
  title =        "Applying layered multi-population genetic programming
                 on learning to rank for information retrieval",
  year =         "2012",
  volume =       "5",
  pages =        "1754--1759",
  size =         "6 pages",
  abstract =     "Information retrieval (IR) returns a relative ranking
                 of documents with respect to a user query. Learning to
                 rank for information retrieval (LR4IR) employs
                 supervised learning techniques to address this problem,
                 and it aims to produce a ranking model automatically
                 for defining a proper sequential order of related
                 documents based on the query. The ranking model
                 determines the relationship degree between documents
                 and the query. In this paper an improved version of
                 RankGP is proposed. It uses layered multi-population
                 genetic programming to obtain a ranking function which
                 consists of a set of IR evidences and particular
                 predefined operators. The proposed method is capable to
                 generate complex functions through evolving small
                 populations. In this paper, LETOR 4.0 was used to
                 evaluate the effectiveness of the proposed method and
                 the results showed that the method is competitive with
                 other LR4IR Algorithms.",
  keywords =     "genetic algorithms, genetic programming, document
                 handling, learning (artificial intelligence), query
                 processing, LETOR 4.0, LR4IR, RankGP, document ranking,
                 layered multipopulation genetic programming, learning
                 to rank for information retrieval, ranking function,
                 supervised learning techniques, user query, Abstracts,
                 Programming, Sociology, Statistics, Evolutionary
                 computation, Learning to rank for Information
                 Retrieval, Ranking function",
  DOI =          "doi:10.1109/ICMLC.2012.6359640",
  ISSN =         "2160-133X",
  notes =        "Also known as \cite{6359640}",

Genetic Programming entries for Mick Jung-Yi Lin Jen-Yuan Yeh Chao Chung Liu