Journal
IEEE TRANSACTIONS ON SERVICES COMPUTING
Volume 13, Issue 4, Pages 685-695Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TSC.2020.2964552
Keywords
Collaboration; Internet of Things; Data models; Correlation; Clustering algorithms; Big Data; Recommender systems; collaborative filtering; clustering model; personalized recommendation
Funding
- National Key Research and Development Program of China [2018YFC1604000]
- National Natural Science Foundation of China [61806138, U1636220, 61961160707, 61976212]
- Key R&D program of Shanxi Province (International Cooperation) [201903D421048]
- Key R&D program of Shanxi Province (High Technology) [201903D121119]
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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 a 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 MovieLens and Douban, and the precision of our model have improved about 5.2 percent compared with the MCoC model. Systematic experimental results have demonstrated our models TCCF and PTCCF are effective for IoT scenarios.
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