A NMF-based Collaborative Filtering Recommendation Algorithm

Recommender systems are becoming increasingly popular with the evolution of the Internet, and collaborative filtering (CF) is one of the most important technologies in recommender systems. Such technology recommends items to a customer according to the preference data of similar customers. The performance of CF systems degrades with increasing number of customers and items. To reduce the dimensionality of filtering databases and to improve the performance, non-negative matrix factorization (NMF) is applied to CF. The experiment results show that NMF-based CF can improve the performance of CF systems in both the recommendation quality and efficiency

[1]  Ravi Kumar,et al.  Recommendation systems: a probabilistic analysis , 1998, Proceedings 39th Annual Symposium on Foundations of Computer Science (Cat. No.98CB36280).

[2]  Thomas Hofmann,et al.  Latent semantic models for collaborative filtering , 2004, TOIS.

[3]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[4]  Nanning Zheng,et al.  Non-negative matrix factorization based methods for object recognition , 2004, Pattern Recognit. Lett..

[5]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.

[6]  Alan F. Smeaton,et al.  Improving the Quality of the Personalized Electronic Program Guide , 2004, User Modeling and User-Adapted Interaction.

[7]  Ravi Kumar,et al.  Recommendation Systems , 2001 .

[8]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[9]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[10]  Michael W. Berry,et al.  Document clustering using nonnegative matrix factorization , 2006, Inf. Process. Manag..

[11]  Sergio A. Alvarez,et al.  Efficient Adaptive-Support Association Rule Mining for Recommender Systems , 2004, Data Mining and Knowledge Discovery.

[12]  Philip S. Yu.  Editorial: State of the Transactions , 2004, IEEE Trans. Knowl. Data Eng..

[13]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[14]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[15]  Loriene Roy,et al.  Content-based book recommending using learning for text categorization , 1999, DL '00.

[16]  Edward I. George,et al.  A bayesian model for collaborative filtering , 1999, AISTATS.

[17]  Hans-Peter Kriegel,et al.  Ieee Transactions on Knowledge and Data Engineering Probabilistic Memory-based Collaborative Filtering , 2022 .

[18]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[19]  Luo Si,et al.  Flexible Mixture Model for Collaborative Filtering , 2003, ICML.

[20]  Benjamin M. Marlin,et al.  Modeling User Rating Profiles For Collaborative Filtering , 2003, NIPS.

[21]  Michael J. Pazzani,et al.  Learning and Revising User Profiles: The Identification of Interesting Web Sites , 1997, Machine Learning.