Feature Extraction for Collaborative Filtering: A Genetic Programming Approach

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

  author =       "Deepa Anand",
  title =        "Feature Extraction for Collaborative Filtering: A
                 Genetic Programming Approach",
  journal =      "International Journal of Computer Science Issues",
  year =         "2012",
  volume =       "9",
  number =       "5",
  pages =        "348--354",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, Recommender
                 Systems, Collaborative Filtering, Feature Extraction",
  publisher =    "IJCSI Press",
  ISSN =         "16940784",
  bibsource =    "OAI-PMH server at www.doaj.org",
  language =     "eng",
  oai =          "oai:doaj-articles:3fdb924cd5e50b8f275ce58daca88188",
  URL =          "http://www.ijcsi.org/contents.php?volume=9&&issue=5",
  URL =          "http://www.ijcsi.org/papers/IJCSI-9-5-1-348-354.pdf",
  size =         "7 pages",
  abstract =     "Collaborative filtering systems offer customised
                 recommendations to users by exploiting the
                 interrelationships between users and items. Users are
                 assessed for their similarity in tastes and items
                 preferred by similar users are offered as
                 recommendations. However scalability and scarcity of
                 data are the two major bottlenecks to effective
                 recommendations. With web based RS typically having
                 users in order of millions, timely recommendations pose
                 a major challenge. Sparsity of ratings data also
                 affects the quality of suggestions. To alleviate these
                 problems we propose a genetic programming approach to
                 feature extraction by employing GP to convert from
                 user-item space to user-feature preference space where
                 the feature space is much smaller than the item space.
                 The advantage of this approach lies in the reduction of
                 sparse high dimensional preference information into a
                 compact and dense low dimensional preference data. The
                 features are constructed using GP and the individuals
                 are evolved to generate the most discriminative set of
                 features. We compare our approach to content based
                 feature extraction approach and demonstrate the
                 effectiveness of the GP approach in generating the
                 optimal feature set.",

Genetic Programming entries for Deepa Anand