Learning to Rank for Information Retrieval Using Genetic Programming

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

  author =       "Jen-Yuan Yeh and Jung-Yi Lin and Hao-Ren Ke and 
                 Wei-Pang Yang",
  title =        "Learning to Rank for Information Retrieval Using
                 Genetic Programming",
  booktitle =    "SIGIR 2007 workshop: Learning to Rank for Information
  year =         "2007",
  editor =       "Thorsten Joachims and Hang Li and Tie-Yan Liu and 
                 ChengXiang Zhai",
  month =        "27 " # jul,
  organisation = "Microsoft",
  keywords =     "genetic algorithms, genetic programming, learning to
                 rank for IR, ranking function, Information Storage and
                 Retrieval, Information Search and Retrieval, Retrieval
                 Models, Algorithms, Experimentation, Performance",
  URL =          "http://jenyuan.yeh.googlepages.com/jyyeh-LR4IR07.pdf",
  size =         "8 pages",
  abstract =     "One central problem of information retrieval (IR) is
                 to determine which documents are relevant and which are
                 not to the user information need. This problem is
                 practically handled by a ranking function which defines
                 an ordering among documents according to their degree
                 of relevance to the user query. This paper discusses
                 work on using machine learning to automatically
                 generate an effective ranking function for IR. This
                 task is referred to as learning to rank for IR in the
                 field. In this paper, a learning method, RankGP, is
                 presented to address this task. RankGP employs genetic
                 programming to learn a ranking function by combining
                 various types of evidences in IR, including content
                 features, structure features, and query-independent
                 features. The proposed method is evaluated using the
                 LETOR benchmark datasets and found to be competitive
                 with Ranking SVM and RankBoost.",
  notes =        "https://research.microsoft.com/en-us/um/beijing/events/LR4IR-2007/",

Genetic Programming entries for Jen-Yuan Yeh Mick Jung-Yi Lin Hao-Ren Ke Wei-Pang Yang