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• W3205260957 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." @default.
• W3205260957 created "2021-10-25" @default.
• W3205260957 creator A5027182927 @default.
• W3205260957 creator A5088325288 @default.
• W3205260957 date "2021-10-14" @default.
• W3205260957 modified "2023-10-18" @default.
• W3205260957 title "A Data-Driven Personalized Lighting Recommender System" @default.
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• W3205260957 doi "https://doi.org/10.3389/fdata.2021.706117" @default.
• W3205260957 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8552333" @default.
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• W3205260957 hasPublicationYear "2021" @default.
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