The weighted majority algorithm
Information and Computation
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
SWAMI (poster session): a framework for collaborative filtering algorithm development and evaluation
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Latent class models for collaborative filtering
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A comparison of several predictive algorithms for collaborative filtering on multi-valued ratings
Proceedings of the 2004 ACM symposium on Applied computing
CROC: A New Evaluation Criterion for Recommender Systems
Electronic Commerce Research
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The basic objective of a Collaborative Filtering (CF) algorithm is to suggest items to a particular user based on his/her preferences and users with similar interests. Although, there is an apparently strong demand for CF techniques, and many algorithms have been recently proposed, very few articles comparing these techniques can be found. Our paper is oriented towards the study of a sample of algorithms to representing differents stages in the evolutive process of CF.Experiments were conducted on two datasets with different characteristics, using two protocols and three evaluation metrics for the different algorithms. The results indicate that, in general, the Online-Learning (WMA, MWM) and the Support Vector Machines algorithms have a better performance that the other algorithms, on both datasets. Considering the amount of information, the less sparse such information is, the higher the coverage and accuracy of general models tend to be; however, the behavior under sparse data is closer to what is observed in a real system if we have in mind that users usually rate an amount of records much smaller than the total available.