  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.
