Content-targeted advertising using genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Delfianto:2011:ICEEI,
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author = "Rizky Delfianto and Masayu Leylia Khodra and
Aristama Roesli",
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title = "Content-targeted advertising using genetic
programming",
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booktitle = "International Conference on Electrical Engineering and
Informatics (ICEEI 2011)",
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year = "2011",
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month = "17-19 " # jul,
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address = "Bandung, Indonesia",
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size = "5 pages",
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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.",
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keywords = "genetic algorithms, genetic programming, GP, Internet,
Web page content, ads placement technique, content
targeted advertising, structural information, Internet,
advertising data processing",
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DOI = "doi:10.1109/ICEEI.2011.6021592",
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ISSN = "2155-6822",
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notes = "Also known as \cite{6021592}",
- }
Genetic Programming entries for
Rizky Delfianto
Masayu Leylia Khodra
Aristama Roesli
Citations