Designing an Explanation Interface for Proactive Recommendations in Automotive Scenarios

Recommender techniques are commonly applied to ease the selection process of items and support decision making. Typically, recommender systems are used in contexts where users focus their full attention to the system. This is not the case in automotive scenarios such as gas station recommendation. We want to provide recommendations proactively to reduce driver distraction while searching for information. Proactively delivered recommendations may not be accepted, if the driver does not understand why something was recommended to her. Therefore, our goal in this paper is to enhance transparency of proactively delivered recommendations by means of explanations. We focus on explaining items to convince the user of the relevance of the items and to enable an efficient item selection during driving. We describe a method based on knowledge- and utility-based recommender systems to extract explanations automatically. Our evaluation shows that explanations enable fast decision making for items with reduced information provided to the user. We also show the design of the system in an in-car navigation system.