A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
The nature of statistical learning theory
The nature of statistical learning theory
Matrix computations (3rd ed.)
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Recommender systems using linear classifiers
The Journal of Machine Learning Research
Personalization of user profiles for content-based music retrieval based on relevance feedback
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Gaussian Processes for Ordinal Regression
The Journal of Machine Learning Research
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Support vector machines for collaborative filtering
Proceedings of the 44th annual Southeast regional conference
Restricted Boltzmann machines for collaborative filtering
Proceedings of the 24th international conference on Machine learning
Efficient bayesian hierarchical user modeling for recommendation system
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Major components of the gravity recommendation system
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Non-linear matrix factorization with Gaussian processes
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Large-scale collaborative prediction using a nonparametric random effects model
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast nonparametric matrix factorization for large-scale collaborative filtering
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Scalable Collaborative Filtering Approaches for Large Recommender Systems
The Journal of Machine Learning Research
Collaborative filtering with the simple Bayesian classifier
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
IEEE Transactions on Neural Networks
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
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Collaborative filtering has been widely applied in many fields in recent years due to the increase in web-based activities such as e-commerce and online content distribution. Current collaborative filtering techniques such as correlation-based, SVD-based and supervised learning-based approaches provide good accuracy, but are computationally very expensive and can only be deployed in static off-line settings, where the known rating information does not change with time. However, a number of practical scenarios require dynamic adaptive collaborative filtering that can allow new users, items and ratings to enter the system at a rapid rate. In this paper, we consider a novel adaptive personalized recommendation based on adaptive learning. Fast adaptive learning runs through all the aspects of the proposed approach, including training, prediction and updating. Empirical evaluation of our approach on Movielens dataset demonstrates that it is possible to obtain accuracy comparable to that of the correlation-based, SVD-based and supervised learning-based approaches at a much lower computational cost.