Association analysis is an effective data mining approach capable of unveiling interesting associations within a large dataset. Although widely adopted in e-business areas, it still has many difficulties when applied in practice. For instance, there is a mismatch between the static rules discovered and the drifting nature of the user interests, and it is difficult to detect associations from a huge volume of raw user data. This paper presents an effective approach to mine evolving association rules in order to tackle these problems. It is followed by a recommendation model based on the evolving association rules unveiled. Experimental results on an online toggery show that it can effectively unveil people’s shifting interests and make better recommendations accordingly.
LONG Shun (龙舜), ZHU Wei-heng (朱蔚恒)
. Mining Evolving Association Rules for E-Business Recommendation[J]. Journal of Shanghai Jiaotong University(Science), 2012
, 17(2)
: 161
-165
.
DOI: 10.1007/s12204-012-1246-4
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