Abstract
The Internet of things services emerge recommendation system as an integral component providing a more reliable service for users and supporting users to get data anyplace. Nevertheless, the conventional recommendation prototype cannot satisfy users’ precise and fast recommended conditions in the (IoT) Internet of things ecosystem. Finding a community by comparing actual user data results against a low recommendation ability during large-volume data features. Besides, the conventional recommendation method neglects the essential link between the user's time and choice. Over time, user importance fluctuates in actuality. With the variation of time, the smart recommendation scheme should accommodate users’ precise and quick service. We propose an innovative smart recommendation system design that relied on the time correlation coefficient (TCC) to approach this prompt service. This smart system also integrates with a developed K-means by cuckoo search (CSK-means), named TCCF. The clustering technique can gather related users collectively for an additional specific and quick reference. Furthermore, a personalized and efficient recommendation paradigm that relied on the (PTCCF) preference pattern model develops the TCCF's quality better than the traditional one. Through examining the user's responses, it can present a more robust feature recommendation. We conducted two entire information sets of Douban & MovieLens through the vast experiments. Our design's accuracy has increased by approximately 5.2 in percentage. Precise test outcomes have revealed that for the IoT scenario, PTCCF and TCCF are adequate.
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Biswas, A., Guha Roy, D. (2022). Personalized Smart Recommendation System for Industrial Internet of Things. In: Mandal, J.K., Buyya, R., De, D. (eds) Proceedings of International Conference on Advanced Computing Applications. Advances in Intelligent Systems and Computing, vol 1406. Springer, Singapore. https://doi.org/10.1007/978-981-16-5207-3_3
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DOI: https://doi.org/10.1007/978-981-16-5207-3_3
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