title: |
An Efficient Association Rule Mining Me-thod for Personalized Recommendation in Mobile E-commerce |
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publication: |
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part of series: |
Advances in Intelligent Systems Research | |
ISBN: |
978-90-78677-40-6 | |
ISSN: |
1951-6851 | |
DOI: |
doi:10.2991/icebi.2010.50 (how to use a DOI) | |
author(s): |
Xiaoyi Deng, Chun Jin, Yoshiyuki Higuchi, C.Jim Han |
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publication date: |
December 2010 |
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keywords: |
Association rules mining,
Transaction matrix, Interestingness, Personalized recommendation, Mobile ecommerce |
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abstract: |
The association rule mining (ARM) is an
important method to solve personalized
recommendation problem in e-commerce.
However, when applied in personalized
recommendation system in mobile ecommerce(MEC), traditional ARMs are
with low mining efficiency and accuracy.
To enhance the efficiency in obtaining
frequent itemsets and the accuracy of
rules mining, this paper proposes an algorithm based on matrix and interestingness,
named MIbARM, which only scans the
database once, can deletes infrequent
items in the mining process to compressing searching space. Finally, experiments
among Apriori, CBAR and BitTableFI
with two synthetic datasets and 64 different parameter combinations were carried
out to verify MIbARM. The results show
that the MIbARM succeed to avoid redundant candidate itemsets and significantly reduce the number of redundant
rules, and it is efficient and effective for
personalized recommendation in MEC. |
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copyright: |
©
Atlantis Press. This article is distributed under the
terms of the Creative Commons Attribution License, which permits
non-commercial use, distribution and reproduction in any medium,
provided the original work is properly cited. |
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full text: |