Communications of the ACM
interactions
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Technology probes: inspiring design for and with families
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Notes towards an ethnography of domestic technology
DIS '02 Proceedings of the 4th conference on Designing interactive systems: processes, practices, methods, and techniques
DPPI '03 Proceedings of the 2003 international conference on Designing pleasurable products and interfaces
Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers
User Modeling and User-Adapted Interaction
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Proceedings of the 10th international conference on Intelligent user interfaces
Trends in the living room and beyond
EuroITV'07 Proceedings of the 5th European conference on Interactive TV: a shared experience
Playful probing: making probing more fun
INTERACT'07 Proceedings of the 11th IFIP TC 13 international conference on Human-computer interaction
Computers in Entertainment (CIE) - Theoretical and Practical Computer Applications in Entertainment
Are we there yet? a probing study to inform design for the rear seat of family cars
INTERACT'11 Proceedings of the 13th IFIP TC 13 international conference on Human-computer interaction - Volume Part II
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Today recommendations are used to improve the quality and the number of interactive TV services offered by providers all over the world. Their main goal is to recommend TV shows and TV events, sometimes combined with an automatic recording function. With the growing number of IPTV offers, the usage of recommendation systems will increase and help to personalize and individualize the TV viewing experience. This article studies recommendations for the home context investigating daily living habits and routines in 40 households in depth using playful and creative cultural probing. Study results are present design recommendations for the development of new forms of recommendation systems. Main results find that users prefer individualized recommendations in-time, either automated or user-oriented, but mostly prefer individualized recommendations for each member of a household rather than personalized for the whole household.