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Plug-in hybrid (PHEV) technology offers the ability to achieve zero tailpipe emissions coupled with convenient refueling. Fleet adoption of PHEVs, often motivated by organizational and regulatory sustainability targets, may not always align with optimal use cases. In a car rental application, barriers to improving fuel economy over a conventional hybrid include: diminished benefits of additional battery capacity on long-distance trips, sparse electric charging infrastructure at the fleet location, lack of renter understanding of electric charging options, and a principle-agent problem where the driver accrues fewer benefits than costs for actions that improve fuel economy, like charging and eco-driving. This study uses high-resolution driving data collected from twelve Ford Fusion Energi sedans owned by University of California, Davis (UC Davis), where the vehicles are rented out for university-related activities.

Recommender systems guide a user to useful objects in a large space of possible options in a personalized way. In this paper, we study recommender systems for vehicles. Compared to previous research on recommender systems in other domains (e.g., movies or music), there are two major challenges associated with recommending vehicles. First, typical customers purchase fewer cars than movies or pieces of music. Thus, it is difficult to obtain rich information about a customer’s vehicle purchase history. Second, content information obtained about a customer (e.g., demographics, vehicle preferences, etc.) is also difficult to acquire during a relatively short stay in a dealership. To address these two challenges, we propose an interactive vehicle recommender system based a novel machine learning method that integrates decision trees and multi-armed bandits. Decision tree learning effectively selects important questions to ask the customer and encodes the customer's key preferences.