Fab: content-based, collaborative recommendation
Communications of the ACM
Selection of distance metrics and feature subsets for K-nearest neighbor classifiers
Selection of distance metrics and feature subsets for K-nearest neighbor classifiers
Collaborative Filtering Using Weighted Majority Prediction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Knowledge and Information Systems
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Extended latent class models for collaborative recommendation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Incremental collaborative filtering for mobile devices
Proceedings of the 2005 ACM symposium on Applied computing
ITWP'03 Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization
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Information filtering is an area getting more important as we have long been flooded with too much information. Product brokering in e-commerce is a typical example and systems which can recommend products to their users in a personalized manner have been studied rigoriously in recent years. Collaborative filtering is one of the commonly used approaches where careful choices of the user similarity measure and the rating style representation are required, and yet there is no guarantee for their optimality. In this paper, we propose the use of machine learning techniques to learn the user similarity as well as the rating style. A criterion function measuring the prediction errors is used and several problem formulations are proposed together with their learning algorithms. We have evaluated our proposed methods using the EachMovie dataset and succeeded in obtaining significant improvement in recommendation accuracy when compared with the standard correlation method.