Modelling User Preferences with Multi-Instance Genetic Programming

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

  author =       "Amelia Zafra and Sebastian Ventura",
  title =        "Modelling User Preferences with Multi-Instance Genetic
  booktitle =    "Information processing and Management of Uncertainty
                 in Knowledge based systems, IPMU 20018",
  year =         "2008",
  editor =       "Luis Magdalena and Jose Luis Verdegay",
  address =      "Malaga, Spain",
  month =        jun # " 22-27",
  keywords =     "genetic algorithms, genetic programming, user
                 modelling, recommender systems, multi-instance
                 learning, multi- objective algorithms",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:",
  rights =       "Metadata may be used without restrictions as long as
                 the oai identifier remains attached to it.",
  URL =          "",
  URL =          "",
  URL =          "",
  abstract =     "In this paper we introduce a novel model for providing
                 users with recommendations about web index pages of
                 their interests. The approach proposed developes user
                 profiles based on evolutionary multi instance learning
                 which determines what users find interesting and
                 uninteresting by means of rules which add
                 comprehensibility and clarity to user models and
                 increase the quality of the recommendations.
                 Experimental results show that our methodology achieves
                 competitive results, providing high-quality user models
                 which improve the accuracy of recommendations.",
  notes =        "",

Genetic Programming entries for Amelia Zafra Gomez Sebastian Ventura