The role of transparency in recommender systems
CHI '02 Extended Abstracts on Human Factors in Computing Systems
Supporting Context-Aware Media Recommendations for Smart Phones
IEEE Pervasive Computing
Context-Aware Computing Applications
WMCSA '94 Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applications
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the fourth ACM conference on Recommender systems
REQUEST: A Query Language for Customizing Recommendations
Information Systems Research
Recommender systems at the long tail
Proceedings of the fifth ACM conference on Recommender systems
Smart books: adding context-awareness and interaction to electronic books
Proceedings of the 9th International Conference on Advances in Mobile Computing and Multimedia
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As the amount of ubiquitous product and service information within our daily lives is exploding, client-centric and context-aware information filtering is one of the thriving topics within the next years. A popular approach is to combine context-awareness with traditional recommendation engines in order to evaluate the relevance of a large amount of items for a given situation and user. Within this work we propose a general software architecture as well as a prototypical implementation for a framework that combines traditional recommendation methods with a variable number of context dimensions, such as location of social context. This work shows how to use a MapReduce programming model for aggregating the necessary information for calculating fast context-aware recommendations. A use-case at the end of this work shows how to use this general framework to implement a client-centric, MapReduce-based recommendation engine for real-time recommending music events.