An Approach to Extract Informative Rules for Web Page Recommendation by Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{journals/ieicet/KimYL12,
  author =       "Jaekwang Kim and KwangHo Yoon and Jee-Hyong Lee",
  title =        "An Approach to Extract Informative Rules for Web Page
                 Recommendation by Genetic Programming",
  journal =      "IEICE Transactions on Communications",
  year =         "2012",
  number =       "5",
  volume =       "95-B",
  pages =        "1558--1565",
  note =         "Special Section on Frontiers of Information Network
                 Science",
  keywords =     "genetic algorithms, genetic programming, association
                 rule mining, web page recommendation, clickstream, user
                 navigational log, context",
  ISSN =         "1745-1345",
  URL =          "http://search.ieice.org/bin/summary.php?id=e95-b_5_1558",
  DOI =          "doi:10.1587/transcom.E95.B.1558",
  abstract =     "Clickstreams in users' navigation logs have various
                 data which are related to users' web surfing. Those are
                 visit counts, stay times, product types, etc. When we
                 observe these data, we can divide click streams into
                 sub-clickstreams so that the pages in a sub-clickstream
                 share more contexts with each other than with the pages
                 in other sub-clickstreams. In this paper, we propose a
                 method which extracts more informative rules from
                 clickstreams for web page recommendation based on
                 genetic programming and association rules. First, we
                 split clickstreams into sub-clickstreams by contexts
                 for generating more informative rules. In order to
                 split clickstreams in consideration of context, we
                 extract six features from users' navigation logs. A set
                 of split rules is generated by combining those features
                 through genetic programming, and then informative rules
                 for recommendation are extracted with the association
                 rule mining algorithm. Through experiments, we verify
                 that the proposed method is more effective than the
                 other methods in various conditions.",
  bibdate =      "2012-05-03",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/ieicet/ieicet95b.html#KimYL12",
}

Genetic Programming entries for Jaekwang Kim KwangHo Yoon Jee-Hyong Lee

Citations