Learning to rank: new approach with the layered multi-population genetic programming on click-through features

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Amir Hosein Keyhanipour and Behzad Moshiri and 
                 Farhad Oroumchian and Maseud Rahgozar and Kambiz Badie",
  title =        "Learning to rank: new approach with the layered
                 multi-population genetic programming on click-through
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2016",
  volume =       "17",
  number =       "3",
  pages =        "203--230",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, Learning to
                 rank, Click, through data Layered multi-population
                 genetic programming",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-016-9263-y",
  size =         "28 pages",
  abstract =     "Users' click-through data is a valuable source of
                 information about the performance of Web search
                 engines, but it is included in few datasets for
                 learning to rank. In this paper, inspired by the
                 click-through data model, a novel approach is proposed
                 for extracting the implicit user feedback from evidence
                 embedded in benchmarking datasets. This process outputs
                 a set of new features, named click-through features.
                 Generated click-through features are used in a layered
                 multi-population genetic programming framework to find
                 the best possible ranking functions. The layered
                 multi-population genetic programming framework is fast
                 and provides more extensive search capability compared
                 to the traditional genetic programming approaches. The
                 performance of the proposed ranking generation
                 framework is investigated both in the presence and in
                 the absence of explicit click-through data in the
                 benchmark datasets. The experimental results show that
                 click-through features can be efficiently extracted in
                 both cases but that more effective ranking functions
                 result when click-through features are generated from
                 benchmark datasets with explicit click-through data. In
                 either case, the most noticeable ranking improvements
                 are achieved at the tops of the provided ranked lists
                 of results, which are highly targeted by the Web

Genetic Programming entries for Amir Hosein Keyhanipour Behzad Moshiri Farhad Oroumchian Maseud Rahgozar Kambiz Badie