Efficient user preference predictions using collaborative filtering

Yang Song, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Two major challenges in collaborative filtering are the efficiency of the algorithms and the quality of the recommendations. A variety of machine learning methods have been applied to address these two issues, including feature selection, instance selection, and clustering. Most existing methods either compromise computational complexity or prediction precision. Two novel, scalable memory-based CF algorithms are proposed, namely BS1, BS2, which combine the strengths of existing techniques while discarding their weaknesses. Experiments show that both the efficiency and performance have been improved when compared to three classical techniques: VSIM, FCBF and PD.

Original languageEnglish (US)
Title of host publication2008 19th International Conference on Pattern Recognition, ICPR 2008
StatePublished - Dec 1 2008
Event2008 19th International Conference on Pattern Recognition, ICPR 2008 - Tampa, FL, United States
Duration: Dec 8 2008Dec 11 2008

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Other

Other2008 19th International Conference on Pattern Recognition, ICPR 2008
CountryUnited States
CityTampa, FL
Period12/8/0812/11/08

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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