Abstract
Recommender systems attempt to identify and recommend the most preferable item (product-service) to individual users. These systems predict user interest in items based on related items, users, and the interactions between items and users. We aim to build an auto-routine and color scheme recommender system for home-based smart lighting that leverages a wealth of historical data and machine learning methods. We utilize an unsupervised method to recommend a routine for smart lighting. Moreover, by analyzing users’ daily logs, geographical location, temporal and usage information, we understand user preferences and predict their preferred light colors. To do so, users are clustered based on their geographical information and usage distribution. We then build and train a predictive model within each cluster and aggregate the results. Results indicate that models based on similar users increases the prediction accuracy, with and without prior knowledge about user preferences.
Highlights
The technological revolution has facilitated the rise of smart ecosystems where every aspect of everyday life such as transportation, agriculture, logistics, and healthcare are automated and can be controlled and managed in the context of smart cities Ahad et al (2020)
This paper proposes a data-driven personalized light recommendation system in a smart lighting device adjusted to residential requirements
Energy consumption is a crucial factor in house lighting products, and smart lighting has a high potential for energy saving Von Neida et al (2001)
Summary
The technological revolution has facilitated the rise of smart ecosystems where every aspect of everyday life such as transportation, agriculture, logistics, and healthcare are automated and can be controlled and managed in the context of smart cities Ahad et al (2020). Smart cities, assisted by modern digital technologies, are a potential solution to enhanced quality and performance of urban service Sikder et al (2018). The introduction of Internet-of-Things (IoT) in smart cities brings opportunities to interconnect different applications using information and communication technology. One of many potentials of smart lighting systems is energy saving capabilities. Smart lighting can be used to increase light quality and have a positive impact on productivity Karlicek (2012), and circadian rhythm Oh et al (2014)
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