Learning to rank for information retrieval using layered multi-population genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Lin:2012:CyberneticsCom,
  author =       "Jung Yi Lin and Jen-Yuan Yeh and Chao Chung Liu",
  booktitle =    "IEEE International Conference on Computational
                 Intelligence and Cybernetics (CyberneticsCom 2012)",
  title =        "Learning to rank for information retrieval using
                 layered multi-population genetic programming",
  year =         "2012",
  pages =        "45--49",
  DOI =          "doi:10.1109/CyberneticsCom.2012.6381614",
  size =         "5 pages",
  abstract =     "To determine which documents are relevant and which
                 are not to the user query is one central problem
                 broadly studied in the field of information retrieval
                 (IR). Learning to rank for information retrieval
                 (LR4IR), which leverages supervised learning-based
                 methods to address the problem, aims to produce a
                 ranking model automatically for defining a proper
                 sequential order of related documents according to the
                 given query. The ranking model is employed to determine
                 the relationship degree between one document and the
                 user query, based on which a ranking of query-related
                 documents could be produced. In this paper we proposed
                 an improved RankGP algorithm using multi-layered
                 multi-population genetic programming to obtain a
                 ranking function, trained from collections of IR
                 results with relevance judgements. In essence, the
                 generated ranking function is consisted of a set of IR
                 evidences (or features) and particular predefined GP
                 operators. The proposed method is capable of generating
                 complex functions through evolving small populations.
                 LETOR 4.0 was used to evaluate the effectiveness of the
                 proposed method and the results showed that the method
                 is competitive with RankSVM and AdaRank.",
  keywords =     "genetic algorithms, genetic programming, document
                 handling, learning (artificial intelligence), query
                 processing, AdaRank, GP operator, LETOR 4.0, LR4IR,
                 RankGP algorithm, RankSVM, learning to rank for
                 information retrieval, miltilayered multipopulation
                 genetic programming, query-related document, ranking
                 model, relevance judgment, supervised learning, support
                 vector machines, user query, Feature extraction,
                 Information retrieval, Machine learning, Sociology,
                 Statistics, Training, Vectors, Learning to rank for
                 Information Retrieval, evolutionary computation,
                 ranking function",
  notes =        "Also known as \cite{6381614}",
}

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

Citations