Nonlinear ranking function representations in genetic programming-based ranking discovery for personalized search

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

  title =        "Nonlinear ranking function representations in genetic
                 programming-based ranking discovery for personalized
  author =       "Weiguo Fan and Praveen Pathak and Linda Wallace",
  journal =      "Decision Support Systems",
  year =         "2006",
  number =       "3",
  volume =       "42",
  pages =        "1338--1349",
  month =        dec,
  bibdate =      "2007-01-23",
  bibsource =    "DBLP,
  keywords =     "genetic algorithms, genetic programming, Information
                 routing, Information retrieval, Ranking function",
  DOI =          "doi:10.1016/j.dss.2005.11.002",
  abstract =     "Ranking function is instrumental in affecting the
                 performance of a search engine. Designing and
                 optimising a search engine's ranking function remains a
                 daunting task for computer and information scientists.
                 Recently, genetic programming (GP), a machine learning
                 technique based on evolutionary theory, has shown
                 promise in tackling this very difficult problem.
                 Ranking functions discovered by GP have been found to
                 be significantly better than many of the other existing
                 ranking functions. However, current GP implementations
                 for ranking function discovery are all designed using
                 the Vector Space model in which the same term weighting
                 strategy is applied to all terms in a document. This
                 may not be an ideal representation scheme at the
                 individual query level considering the fact that many
                 query terms should play different roles in the final
                 ranking. In this paper, we propose a novel nonlinear
                 ranking function representation scheme and compare this
                 new design to the well-known Vector Space model. We
                 theoretically show that the new representation scheme
                 subsumes the traditional Vector Space model
                 representation scheme as a special case and hence
                 allows for additional flexibility in term weighting. We
                 test the new representation scheme with the GP-based
                 discovery framework in a personalised search
                 (information routing) context using a TREC web corpus.
                 The experimental results show that the new ranking
                 function representation design outperforms the
                 traditional Vector Space model for GP-based ranking
                 function discovery.",

Genetic Programming entries for Weiguo Fan Praveen Pathak Linda G Wallace