Communications of the ACM
Just-in-time information retrieval
Just-in-time information retrieval
Using Physical Context for Just-in-Time Information Retrieval
IEEE Transactions on Computers
Improving proactive information systems
Proceedings of the 10th international conference on Intelligent user interfaces
A Survey of Challenges Related to the Design of 3D User Interfaces for Car Drivers
3DUI '06 Proceedings of the 3D User Interfaces
The effects of transparency on trust in and acceptance of a content-based art recommender
User Modeling and User-Adapted Interaction
Acceptance of future persuasive in-car interfaces towards a more economic driving behaviour
Proceedings of the 1st International Conference on Automotive User Interfaces and Interactive Vehicular Applications
Context-aware information agents for the automotive domain using Bayesian networks
Proceedings of the 2007 conference on Human interface: Part I
Proceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation
Enabling micro-entertainment in vehicles based on context information
Proceedings of the 2nd International Conference on Automotive User Interfaces and Interactive Vehicular Applications
Designing an explanation interface for proactive recommendations in automotive scenarios
UMAP'11 Proceedings of the 19th international conference on Advances in User Modeling
Proceedings of the 4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications
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Modern in-vehicle information systems (IVIS) are able to provide a large amount of data to the driver. If every information which might be of interest is delivered directly to the driver, information overload becomes a serious problem. Recommender systems are a promising approach to reduce information overload but they are mainly designed for desktop systems or mobile devices. In-vehicle recommender systems have to cope with interaction restrictions and limited cognitive resources of the driver. Therefore, we investigate proactive recommender systems, where recommendations are pushed automatically. The contribution of this paper is a user study in a real world setup to investigate the acceptance of a proactive recommender system while driving. The evaluation is based on the Technology Acceptance Model (TAM). As perceived ease of use is crucial for acceptance, we design an in-vehicle user interface for proactive recommendations. Our results show that our proactive recommender is perceived as helpful and assisting and is not obtrusive and distracting while driving. We also found that clear information delivery and trust is crucial for the acceptance of in-vehicle recommendations.