A model for proactivity in mobile, context-aware recommender systems

Authors:
Wolfgang Woerndl;Johannes Huebner;Roland Bader;Daniel Gallego-Vico
Affiliations:
Technische Universitaet Muenchen, Munich, Germany;Technische Universitaet Muenchen, Munich, Germany;BMW Group Research & Technology, Munich, Germany;Universidad Politécnica de Madrid, Madrid, Spain
Venue:
Proceedings of the fifth ACM conference on Recommender systems
Year:
2011

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Abstract

A proactive recommender system pushes recommendations to the user when the current situation seems appropriate, without explicit user request. This is conceivable in mobile scenarios such as restaurant or gas station recommendations. In this paper, we present a model for proactivity in mobile recommender systems. The model relies on domain-dependent context modeling in several categories. The recommendation process is divided into two phases to first analyze the current situation and then examine the suitability of particular items. We have implemented a prototype gas station recommender and conducted a survey for evaluation. Results showed good correlation of the output of our system with the assessment of users regarding the question when to generate recommendations.