Effects of relevant contextual features in the performance of a restaurant recommender system

Contextual information in recommender systems aims to improve user satisfaction. Usually, it is assumed that the complete set of contextual features is significant. However, identifying relevant context variables is important as increasing their number may lead the system to dimensionality problems. In this paper, relevant contextual attributes are identified by using a simple feature selection approach. Once the features has been identified, it is shown their impact in different performance aspects of the system. This approach was applied to a semantic based restaurant recommender system. Results show that feature selection techniques can be applied successfully to identify relevant contextual data. These results are important to model contextual user profiles with meaningful information, to reduce dimensionality, and to analyze user’s decision criteria.