GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
Context-aware recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
A hybrid approach to item recommendation in folksonomies
Proceedings of the WSDM '09 Workshop on Exploiting Semantic Annotations in Information Retrieval
Context aware recommendations for user-generated content on a social network site
Proceedings of the seventh european conference on European interactive television conference
A unified approach to building hybrid recommender systems
Proceedings of the third ACM conference on Recommender systems
RecSys'09 workshop 3: workshop on context-aware recommender systems (CARS-2009)
Proceedings of the third ACM conference on Recommender systems
I tag, you tag: translating tags for advanced user models
Proceedings of the third ACM international conference on Web search and data mining
Proceedings of the Workshop on Context-Aware Movie Recommendation
Workshop on Context-aware Movie Recommendation 2010
Exploiting hierarchical tags for context-awareness
ESAIR '10 Proceedings of the third workshop on Exploiting semantic annotations in information retrieval
Utilizing implicit feedback and context to recommend mobile applications from first use
Proceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation
A mobile 3D-GIS hybrid recommender system for tourism
Information Sciences: an International Journal
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Context-aware recommender systems are becoming a popular topic, still, there are many untouched aspects. In this paper, research involving context identification and the concepts related to hybrid and context-aware systems is presented. A conceptual architecture for a context-aware recommender system for movies and TV shows is furthermore introduced. The system consists of a number of processes for context identification and recommendation. Key contextual features are identified and used for the creation of several sets of recommendations, based on the predicted context. The main focus of the research presented here is the identification of context, which in turn is used for recommendation. The results will be evaluated and incorporated into the recommendation engine of movie and TV recommendation website Moviepilot.