GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Naïve filterbots for robust cold-start recommendations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering on streaming data with interest-drifting
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
Personalized recommendation on dynamic content using predictive bilinear models
Proceedings of the 18th international conference on World wide web
Pairwise preference regression for cold-start recommendation
Proceedings of the third ACM conference on Recommender systems
An effective algorithm for dimensional reduction in collaborative filtering
ICADL'07 Proceedings of the 10th international conference on Asian digital libraries: looking back 10 years and forging new frontiers
Enhanced email spam filtering through combining similarity graphs
Proceedings of the fourth ACM international conference on Web search and data mining
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This paper proposes a novel, unified, and systematic approach to combine collaborative and content-based filtering for ranking and user preference prediction. The framework incorporates all available information by coupling together multiple learning problems and using a suitable kernel or similarity function between user-item pairs. We propose and evaluate an on-line algorithm (JRank)that generalizes perceptron learning using this framework and shows significant improvement over other approaches.