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Recommendation technology is an important part of the Internet of Things (IoT)
services, which can provide better service for users and help users get information
anytime, anywhere. However, the traditional recommendation algorithms cannot meet
user‟s fast and accurate recommended requirements in the IoT environment. In the face
of a large-volume data, the method of finding neighborhood by comparing whole user
information will result in low recommendation efficiency. In addition, the traditional
recommendation system ignores the inherent connection between user‟s preference
and time. In reality, the interest of the user varies over time. Recommendation system
should provide users accurate and fast with the change of time. To address this, we
propose a novel recommendation model based on time correlation coefficient and an
improved K-means with cuckoo search (CSK-means), called TCCF. The clustering
method can cluster similar users together for further quick and accurate
recommendation. Moreover, an effective and personalized recommendation model
based on preference pattern (PTCCF) is designed to improve the quality of TCCF. It
can provide a higher quality recommendation by analyzing the user‟s behaviors. The
extensive experiments are conducted on two real datasets of Movie Lens and Douban,
and the precision of our model have improved about 5.2% compared with the MCoC
model. Systematic experimental results have demonstrated our models TCCF and
PTCCF are effective.

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