A Collaborative Recommender System Based on Space-Time Similarities

  • Authors:
  • Mario Munoz-Organero;Gustavo A. Ramíez-González;Pedro J. Munoz-Merino;Carlos Delgado Kloos

  • Affiliations:
  • Carlos III University of Madrid;Carlos III University of Madrid;Carlos III University of Madrid;Carlos III University of Madrid

  • Venue:
  • IEEE Pervasive Computing
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

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.