Context-aware POI recommendations in an automotive scenario using multi-criteria decision making methods

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.