Issue No. 03 - July-September (2010 vol. 9)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MPRV.2010.56
Mario Muñoz-Organero , Carlos III University of Madrid
Gustavo A. Ramíez-González , Carlos III University of Madrid
Pedro J. Muñoz-Merino , Carlos III University of Madrid
Carlos Delgado Kloos , Carlos III University of Madrid
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.
Recommender systems, Internet of Things, NFC tagged environments, pervasive content, collaborative recommender systems, networking and communications
P. J. Muñoz-Merino, G. A. Ramíez-González, C. Delgado Kloos and M. Muñoz-Organero, "A Collaborative Recommender System Based on Space-Time Similarities," in IEEE Pervasive Computing, vol. 9, no. , pp. 81-87, 2010.