Advances in telecommunications and information technology have allowed the proliferation of mobile and multifunctional devices and their incorporation more and more into physical objects, making new information and services available. A consequent problem of this new scenario is information overload, i.e. users face vast and distributed information sources, and have difficulty in selecting thos...

The latest advance in recommendation shows that better user and item representations can be learned via performing graph convolutions on the user-item interaction graph. However, such finding is mostly restricted to collaborative filtering (CF) scenario, where contexts are not available. In this work, we extend advantages of context-aware recommender system (CARS, which represents a generic typ...

This paper presents a context-aware mobile shopping recommender system. A critique-based baseline recommender system is enhanced by the integration of context conditions like weather, time, temperature and the user’s company. These context conditions are embedded into the recommendation algorithm via preand post-filtering. A nearest neighbor algorithm, using the concept of an average selection ...