Evolving context-aware recommender systems with users in mind

A context-aware recommender system (CARS) utilizes users’ context to provide personalized services. Contextual information can be derived from sensors in order to improve the accuracy of the recommendations. In this work, we focus on CARSs with high-dimensional contextual information that typically impacts the recommendation model, for example, by increasing the model’s dimensionality and sparsity. Generating accurate recommendations is not enough to constitute a useful system from the user’s perspective, since the use of some contextual information may cause problems, such as draining the user’s battery, raising privacy concerns, and more.