The Internet of Things (IoT) concept promises a world of networked and interconnected devices that provides relevant content to users. Recommender systems can find relevant content for users in IoT environments, offering a user-adapted personalized experience. Collaboration-based recommenders in IoT environments rely on user-to-object, space-time interaction patterns. This extension of that idea takes into account user location and interaction time to recommend scattered, pervasive context-embedded networked objects. The authors compare their proposed system to memory-based collaborative methods in which user similarity is based on the ratings of previously rated items. Their proof-of-concept implementation was used in a real-world scenario involving 15 students interacting with 75 objects at Carlos III University of Madrid.
['Mario Munoz-Organero', 'Gustavo A Ramíez-González', 'Pedro J. Muñoz-Merino', 'Carlos Delgado Kloos']
A Collaborative Recommender System Based on Space-Time Similarities