Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Proceedings of the 17th International Conference on Data Engineering
Processing in-route nearest neighbor queries: a comparison of alternative approaches
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
Incorporating contextual information in recommender systems using a multidimensional approach
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Context-aware recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
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KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
A model for proactivity in mobile, context-aware recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Designing an explanation interface for proactive recommendations in automotive scenarios
UMAP'11 Proceedings of the 19th international conference on Advances in User Modeling
Towards personalized context-aware recommendation by mining context logs through topic models
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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
Fast ALS-Based tensor factorization for context-aware recommendation from implicit feedback
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Exploration / exploitation trade-off in mobile context-aware recommender systems
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Context-aware item-to-item recommendation within the factorization framework
Proceedings of the 3rd Workshop on Context-awareness in Retrieval and Recommendation
Comparing context-aware recommender systems in terms of accuracy and diversity
User Modeling and User-Adapted Interaction
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Recommender systems are commonly used for recommending items such as products, restaurants or other points-of-interest (POI). In our automotive scenario, the driver of a car gets recommendations for gas stations. Thereby, item attributes such as price or location are important, but also context data such as the current time, location or gas level of the car when requesting the recommendation. Our approach is based on Multi-Criteria Decision Making (MCDM) methods to calculate scores on several dimensions. We used utility functions modeling the importance of different route context elements which were derived from a preliminary user study, among other information. In addition, our system performs contextual pre- and post-filtering to reduce the number of considered items. The evaluation showed that our approach produced reasonably good results in comparison with the assessment of users in a second study.