Content-targeted advertising using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Rizky Delfianto and Masayu Leylia Khodra and 
                 Aristama Roesli",
  title =        "Content-targeted advertising using genetic
  booktitle =    "International Conference on Electrical Engineering and
                 Informatics (ICEEI 2011)",
  year =         "2011",
  month =        "17-19 " # jul,
  address =      "Bandung, Indonesia",
  size =         "5 pages",
  abstract =     "Content-targeted advertising is an ads placement
                 technique which associates ads to a web page relative
                 to (based on) the content of the web page (web page
                 content). It introduces a challenge about how to settle
                 the conflict of interests by selecting advertisements
                 that are relevant to the users but also profitable to
                 the advertisers and the publishers. This paper proposes
                 an approach to associate ads with web pages using
                 Genetic Programming (GP). GP is an extension of genetic
                 algorithm in which the individual is not a stream of
                 character but rather a program (function). This work is
                 done in two stages. In the first stage, GP is used to
                 learn a ranking function which leverages the structural
                 and non structural information of the ads. The
                 structural parts of the ads are the title and
                 description. These are the parts that are shown when an
                 ad is placed in a web page. The non-structural part is
                 the set of keywords assigned to the ads. This part is
                 used by the advertisers to determine what topic of the
                 web page content should be to have the ads shown on it.
                 The ranking function produced in the first stage is
                 then used to rank ads given content of a web page in
                 the second stage, the content-targeted advertising
                 system. The experiment result showed that the ranking
                 function effectiveness is just a little below the
                 baseline method but its time efficiency is far better
                 than the baseline at almost 12 times better. In spite
                 of its effectiveness deficiency, the ranking function
                 is still more suitable for content-targeted advertising
                 system. The experiment result also proved that the
                 mutation genetic operation contributes to the result of
                 GP learning by creating a better-performed ranking
                 function. The ranking function generated from GP
                 learning which used mutation genetic operation is 0.11
                 more effective than the ranking function generated from
                 GP which did not used mutation genetic operation.",
  keywords =     "genetic algorithms, genetic programming, GP, Internet,
                 Web page content, ads placement technique, content
                 targeted advertising, structural information, Internet,
                 advertising data processing",
  DOI =          "doi:10.1109/ICEEI.2011.6021592",
  ISSN =         "2155-6822",
  notes =        "Also known as \cite{6021592}",

Genetic Programming entries for Rizky Delfianto Masayu Leylia Khodra Aristama Roesli