Explanations in Proactive Recommender Systems in Automotive Scenarios

Recommender techniques are commonly used to ease the selection and support the decision in the context of large quantities of items such as products, media or restaurants. Typically, recommender systems are used in contexts where users focus their full attention to the system. This is not the case in automotive scenarios, therefore we want to provide recommendations proactively to reduce driver distraction while searching for information. Our application scenario is a gas station recommender. Proactively delivered recommendations may will not be accepted, if the user 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 knowledgeand 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.