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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. 相似文献
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