IEICE TRANSACTIONS on Communications
An Approach to Extract Informative Rules for Web Page Recommendation by Genetic Programming
Summary :
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 clickstreams 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.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E95-B No.5 pp.1558-1565
- Publication Date
- 2012/05/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.E95.B.1558
- Type of Manuscript
- Special Section PAPER (Special Section on Frontiers of Information Network Science)
- Category
Authors
Keyword
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Jaekwang KIM, KwangHo YOON, Jee-Hyong LEE, "An Approach to Extract Informative Rules for Web Page Recommendation by Genetic Programming" in IEICE TRANSACTIONS on Communications,
vol. E95-B, no. 5, pp. 1558-1565, May 2012, 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 clickstreams 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.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E95.B.1558/_p
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@ARTICLE{e95-b_5_1558,
author={Jaekwang KIM, KwangHo YOON, Jee-Hyong LEE, },
journal={IEICE TRANSACTIONS on Communications},
title={An Approach to Extract Informative Rules for Web Page Recommendation by Genetic Programming},
year={2012},
volume={E95-B},
number={5},
pages={1558-1565},
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 clickstreams 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.},
keywords={},
doi={10.1587/transcom.E95.B.1558},
ISSN={1745-1345},
month={May},}
Copy
TY - JOUR
TI - An Approach to Extract Informative Rules for Web Page Recommendation by Genetic Programming
T2 - IEICE TRANSACTIONS on Communications
SP - 1558
EP - 1565
AU - Jaekwang KIM
AU - KwangHo YOON
AU - Jee-Hyong LEE
PY - 2012
DO - 10.1587/transcom.E95.B.1558
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E95-B
IS - 5
JA - IEICE TRANSACTIONS on Communications
Y1 - May 2012
AB - 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 clickstreams 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.
ER -
English
