An Approach to Extract Informative Rules for Web Page Recommendation by Genetic Programming
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- @Article{journals/ieicet/KimYL12,
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author = "Jaekwang Kim and KwangHo Yoon and Jee-Hyong Lee",
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title = "An Approach to Extract Informative Rules for Web Page
Recommendation by Genetic Programming",
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journal = "IEICE Transactions on Communications",
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year = "2012",
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number = "5",
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volume = "95-B",
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pages = "1558--1565",
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note = "Special Section on Frontiers of Information Network
Science",
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keywords = "genetic algorithms, genetic programming, association
rule mining, web page recommendation, clickstream, user
navigational log, context",
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ISSN = "1745-1345",
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URL = "http://search.ieice.org/bin/summary.php?id=e95-b_5_1558",
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DOI = "doi:10.1587/transcom.E95.B.1558",
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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.",
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bibdate = "2012-05-03",
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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