Learning Aggregation Functions for Expert Search

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

@InProceedings{DBLP:conf/ecai/CumminsLO10,
  author =       "Ronan Cummins and Mounia Lalmas and Colm O'Riordan",
  title =        "Learning Aggregation Functions for Expert Search",
  year =         "2010",
  booktitle =    "Proceedings of the 19th European Conference on
                 Artificial Intelligence, ECAI 2010",
  editor =       "Helder Coelho and Rudi Studer and Michael Wooldridge",
  volume =       "215",
  series =       "Frontiers in Artificial Intelligence and
                 Applications",
  pages =        "535--540",
  address =      "Lisbon, Portugal",
  month =        aug # " 16-20",
  publisher =    "IOS Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-60750-605-8",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:10.1.1.419.500",
  URL =          "http://ebooks.iospress.nl/publication/5831",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.419.500",
  URL =          "http://ir.dcs.gla.ac.uk/~mounia/Papers/ECAI10.pdf",
  URL =          "http://www.booksonline.iospress.nl/Content/View.aspx?piid=17702",
  URL =          "http://dx.doi.org/10.3233/978-1-60750-606-5-535",
  size =         "6 pages",
  abstract =     "Machine learning techniques are increasingly being
                 applied to problems in the domain of information
                 retrieval and text mining. In this paper we present an
                 application of evolutionary computation to the area of
                 expert search. Expert search in the context of
                 enterprise information systems deals with the problem
                 of finding and ranking candidate experts given an
                 information need (query). A difficult problem in the
                 area of expert search is finding relevant information
                 given an information need and associating that
                 information with a potential expert.

                 We attempt to improve the effectiveness of a benchmark
                 expert search approach by adopting a learning model
                 (genetic programming) that learns how to aggregate the
                 documents/information associated with each expert. In
                 particular, we perform an analysis of the aggregation
                 of document information and show that different numbers
                 of documents should be aggregated for different queries
                 in order to achieve optimal performance.

                 We then attempt to learn a function that optimises the
                 effectiveness of an expert search system by aggregating
                 different numbers of documents for different queries.
                 Furthermore, we also present experiments for an
                 approach that aims to learn the best way to aggregate
                 documents for individual experts. We find that
                 substantial improvements in performance can be
                 achieved, over standard analytical benchmarks, by the
                 latter of these approaches.",
  notes =        "ECAI",
}

Genetic Programming entries for Ronan Cummins Mounia Lalmas Colm O'Riordan

Citations