Content-targeted advertising using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7764

@InProceedings{Delfianto:2011:ICEEI,

• author = "Rizky Delfianto and Masayu Leylia Khodra and Aristama Roesli",
• title = "Content-targeted advertising using genetic programming",
• 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

Citations