An example-based mapping method for text categorization and retrieval
ACM Transactions on Information Systems (TOIS)
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Machine Learning
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Nonlinear Markov networks for continuous variables
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Dependency Networks for Collaborative Filtering and Data Visualization
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Compression by Model Combination
DCC '98 Proceedings of the Conference on Data Compression
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
The context-tree weighting method: basic properties
IEEE Transactions on Information Theory
An Efficient Intelligent Agent System for Automated Recommendation in Electronic Commerce
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Compound Classification Models for Recommender Systems
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
A comprehensive reference model for personalized recommender systems
HI'11 Proceedings of the 2011 international conference on Human interface and the management of information - Volume Part I
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Recommender systems use historical data on user preferences and other available data on users (e.g., demographics) and items (e.g., taxonomy) to predict items a new user might like. Applications of these methods include recommending items for purchase and personalizing the browsing experience on a web-site. Collaborative filtering methods have focused on using just the history of user preferences to make the recommendations. These methods have been categorized as memory-based if they operate over the entire data to make predictions and as model-based if they use the data to build a model which is then used for predictions. In this paper, we propose the use of linear classifiers in a model-based recommender system. We compare our method with another model-based method using decision trees and with memory-based methods using data from various domains. Our experimental results indicate that these linear models are well suited for this application. They outperform the commonly proposed approach using a memory-based method in accuracy and also have a better tradeoff between off-line and on-line computational requirements.