Adaptive user similarity measures for recommender systems: A genetic programming approach

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Anand:2010:ICCSIT,
  author =       "Deepa Anand and K. K. Bharadwaj",
  title =        "Adaptive user similarity measures for recommender
                 systems: A genetic programming approach",
  booktitle =    "3rd IEEE International Conference on Computer Science
                 and Information Technology (ICCSIT 2010)",
  year =         "2010",
  month =        "9-11 " # jul,
  volume =       "8",
  pages =        "121--125",
  abstract =     "Recommender systems signify the shift from the
                 paradigm of searching for items to discovering items
                 and have been employed by an increasing number of
                 e-commerce sites for matching users to their
                 preferences. Collaborative Filtering is a popular
                 recommendation technique which exploits the past
                 user-item interactions to determine user similarity.
                 The preferences of such similar users are leveraged to
                 offer suggestions to the active user. Even though
                 several techniques for similarity assessment have been
                 suggested in literature, no technique has been proven
                 to be optimal under all contexts/data conditions.
                 Hence, we propose a two-stage process to assess user
                 similarity, the first is to learn the optimal
                 transformation function to convert the raw ratings data
                 to preference data by employing genetic programming,
                 and the second is to use the preference values, so
                 derived, to compute user similarity. The application of
                 such learnt user bias gives rise to adaptive similarity
                 measures, i.e. similarity estimates that are dataset
                 dependent and hence expected to work best under any
                 data environment. We demonstrate the superiority of our
                 proposed technique by contrasting it to traditional
                 similarity estimation techniques on four different
                 datasets representing varied data environments.",
  keywords =     "genetic algorithms, genetic programming, adaptive user
                 similarity measure, collaborative filtering, data
                 environment, item discovery, item searching, optimal
                 transformation function, preference value, raw ratings
                 data, recommender system, similarity assessment,
                 similarity estimation, user-item interaction,
                 groupware, information filtering, recommender systems",
  DOI =          "doi:10.1109/ICCSIT.2010.5563737",
  notes =        "Sch. of Comput. & Syst. Sci., Jawaharlal Nehru Univ.,
                 Delhi, India Also known as \cite{5563737}",
}

Genetic Programming entries for Deepa Anand K K Bharadwaj

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