Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers
User Modeling and User-Adapted Interaction
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
Localisation intra-bâtiment multi-technologies: RFID, wifi et vision
UbiMob '05 Proceedings of the 2nd French-speaking conference on Mobility and ubiquity computing
ETRICS'06 Proceedings of the 2006 international conference on Emerging Trends in Information and Communication Security
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This work presents a method and experiments with privacy protection in multimedia and information retrieval system, currently being developed in Amigo project. Since Amigo environment is capable of recognition of user situations (contexts), the recommender system takes into account both long-term user interests and contexts when providing recommendations. We propose to utilize context recognition also for privacy protection, and suggest a method which either allows or suspends recommending of an item via nonpersonal UI, depending on a current context and retrieval history. This work studies how privacy protection affects precision and recall of recommendations. Two privacy-protection techniques were explored: protection based on the user's social context (other people around the user); and protection based on the user's location. Experimental results on data, collected via user interviews, show that social context-based protection works better than the location-based one; and that during normal family life privacy protection does not decrease system performance significantly. Instead, in some cases system performance has been improved.