A data-driven personalized smart lighting recommender system
This is an incremental improvement for smart home users, focusing on enhancing lighting recommendations through data analysis.
The paper tackled the problem of personalized lighting recommendations by developing an unsupervised method to suggest routines and color schemes based on user data, resulting in increased prediction accuracy when clustering similar users.
Recommender systems attempts to identify and recommend the most preferable item (product-service) to an individual user. 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 that leverages a wealth of historical data and machine learning methods. We introduce an unsupervised method to recommend a routine for lighting. Moreover, by analyzing users' daily logs, geographical location, temporal and usage information we understand user preference and predict their preferred color for lights. To do so, we cluster users 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.