Toward Building an Individual Preference Model for Personalizing Settings in the Vehicle

摘要

It is highly expected that your car learns your individual preferences and context so that when it is cold, it automatically switches on the seat heating and when it is warm it switches on the air conditioning. This paper describes a simple computational method for learning individual preferences that has the advantage that it does not require to be trained in the vehicle on-board for every customer separately, while maintaining its ability to tune to every customer individually rather than defaulting to the average customer's preference. Our results show that our method outperforms the non-individual and clustering models, and demonstrates the feasibility of integration into the vehicle.