Movie recommendations based in explicit and implicit features extracted from the Filmtipset dataset
Proceedings of the Workshop on Context-Aware Movie Recommendation
Recommender systems by means of information retrieval
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
Recommender systems at the long tail
Proceedings of the fifth ACM conference on Recommender systems
Harnessing geo-tagged resources for Web personalization
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Improving quality control by early prediction of manufacturing outcomes
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Beyond rating prediction accuracy: on new perspectives in recommender systems
Proceedings of the 7th ACM conference on Recommender systems
Proceedings of the 7th ACM international conference on Web search and data mining
From big smartphone data to worldwide research: The Mobile Data Challenge
Pervasive and Mobile Computing
Multi-objective mobile app recommendation: A system-level collaboration approach
Computers and Electrical Engineering
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Netflix is offering US $1 million for an algorithm that's 10 percent more accurate than the one Netflix uses to predict customers' movie preferences. The AT&T Labs team account is presented.