Just-in-time information retrieval
Just-in-time information retrieval
Trust building with explanation interfaces
Proceedings of the 11th international conference on Intelligent user interfaces
Persuasion in Knowledge-Based Recommendation
PERSUASIVE '08 Proceedings of the 3rd international conference on Persuasive Technology
Generating and evaluating evaluative arguments
Artificial Intelligence
Proceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation
A study on user acceptance of proactive in-vehicle recommender systems
Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications
Review: Mobile recommender systems in tourism
Journal of Network and Computer Applications
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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.