A method for privacy-preserving context-aware mobile recommendations

Mobile recommender systems aim to solve the information overload problem found by recommending products or services to users of mobile smartphones or tables at any given point in time and in any possible location. Mobile recommender systems are designed for the specific goal of mobile recommendations, such as mobile commerce or tourism and are ported to a mobile device for this purpose. They utilize a specific recommendation method, like collaborative filtering or content-based filtering and use a considerable amount of contextual information in order to provide more personalized recommendations. However due to privacy concerns users are not willing to provide the required personal information to make these systems usable. In response to the privacy concerns of users we present a method of privacy preserving context-aware mobile recommendations and show that it is both practical and effective.