Abstract
In this article, we research on the service recommendation strategy in the IoT. The user attribute similarity and the attribute correlation of user and device service are computed, and the recommendation system is recommended based on the calculation results. In order to solve the cold start problem, we propose the tensor linear regression model. The experiment results show the recommendation strategy was effective.
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Acknowledgement
This work is partly supported by the National Natural Science Foundation of China under Grants 61272520, 61370196, 61532012; Scientific Studies Program of Higher Education of Inner Mongolia Municipality (NJZY237).
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Wang, P., Luo, H., Sun, Y. (2017). IOT Service Recommendation Strategy Based on Attribute Relevance. In: Ochoa, S., Singh, P., Bravo, J. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2017. Lecture Notes in Computer Science(), vol 10586. Springer, Cham. https://doi.org/10.1007/978-3-319-67585-5_4
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DOI: https://doi.org/10.1007/978-3-319-67585-5_4
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