Measuring retrieval effectiveness based on user preference of documents
Journal of the American Society for Information Science
Fab: content-based, collaborative recommendation
Communications of the ACM
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Modeling distances in large-scale networks by matrix factorization
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
An MDP-Based Recommender System
The Journal of Machine Learning Research
Eigentaste 5.0: constant-time adaptability in a recommender system using item clustering
Proceedings of the 2007 ACM conference on Recommender systems
SVD based initialization: A head start for nonnegative matrix factorization
Pattern Recognition
Introduction to Information Retrieval
Introduction to Information Retrieval
Short communication: Recommendation based on rational inferences in collaborative filtering
Knowledge-Based Systems
Collaborative filtering adapted to recommender systems of e-learning
Knowledge-Based Systems
A new collaborative filtering metric that improves the behavior of recommender systems
Knowledge-Based Systems
Applying the learning rate adaptation to the matrix factorization based collaborative filtering
Knowledge-Based Systems
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Due to lack of detailed tests under the uniform test framework for existing personalized recommendation algorithms, the best performances claimed in literatures are hard to credit. For this reason, this paper presents a comparative evaluation of eight collaborative filtering (CF) algorithms, mainly focusing on recommendation algorithms related to dimension reduction techniques, on two common popular datasets by using three quality metrics. The eight algorithms are the k-nearest neighbor (KNN) algorithm, three native dimension-reducing algorithms respectively based on singular value decomposition (SVD), non-negative matrix factorization (NMF) and weighted non-negative matrix factorization, and four hybrid algorithms respectively crossing principal component analysis: PCA and KNN, SVD and KNN, NMF and KNN, and PCA and a recursive rectangular clustering. There are some interesting findings in our experiments. First, dimension-reducing techniques can help dig out more valuable information from the rating data than the nearest-neighbor technique. Second, in comparison with four hybrid algorithms, three native algorithms only based on dimension-reducing techniques are able to better satisfy users' actual needs. Third, dimension-reducing techniques with non-negativity constraints are more effective than not with non-negativity ones. Fourth, the decision on the optimum algorithm among eight algorithms is insensitive to the sparsity of dataset. Fifth, proper selections for appropriate values of parameters of algorithms are very often problem dependent. Sixth, native dimension-reducing algorithms can defeat these algorithms related to KNN in the processing time. These findings not only show that it is necessary to evaluate in detail these algorithms under the uniform test framework but also play an important role in the right navigation for further innovation research on the technology of the personalized recommendation.