Adaptive Web Search: Evolving a Program That Finds Information

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  title =        "Adaptive Web Search: Evolving a Program That Finds
  author =       "Michael Gordon and Weiguo (Patrick) Fan and 
                 Praveen Pathak",
  journal =      "IEEE Intelligent Systems",
  year =         "2006",
  volume =       "21",
  number =       "5",
  pages =        "72--77",
  month =        sep # "-" # oct,
  keywords =     "genetic algorithms, genetic programming, Internet,
                 information needs, relevance feedback, search engines,
                 Web pages, adaptive Web search, document relevance
                 feedback, genetic programming, retrieval algorithms,
                 retrieval technique, search engines, user judgement
                 feedback, user persistent information needs",
  ISSN =         "1541-1672",
  DOI =          "doi:10.1109/MIS.2006.86",
  size =         "6 pages",
  abstract =     "Anyone who's used a computer to find information on
                 the Web knows that the experience can be frustrating.
                 Search engines are incorporating new techniques (such
                 as examining document link structures) to increase
                 effectiveness. However, searchers all too often face
                 one of two outcomes: reviewing many more Web pages than
                 they'd prefer or failing to find as much useful
                 information as they really want. We introduce a new
                 retrieval technique that exploits users' persistent
                 information needs. These users might include business
                 analysts specialising in genetic technologies,
                 stockbrokers keeping abreast of wireless
                 communications, and legislators needing to understand
                 computer privacy and security developments. To help
                 such searchers, we evolve effective search programs by
                 using feedback based on users' judgments about the
                 relevance of the documents they've retrieved. This
                 approach uses genetic programming to automatically
                 evolve new retrieval algorithms based on a user's
                 evaluation of previously viewed documents",
  notes =        "IR, cosine nearness measure, keyword weighting.

                 Log. Pop=200. TREC 80000 documents. Large number (500)
                 papers returned to user.

                 GP way better in comparison with SMART (Singhal, 1996)
                 and ANN.",

Genetic Programming entries for Michael D Gordon Weiguo Fan Praveen Pathak