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ICPR
2008
IEEE

Efficient user preference predictions using collaborative filtering

10 years 3 months ago
Efficient user preference predictions using collaborative filtering
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.
C. Lee Giles, Yang Song
Added 05 Nov 2009
Updated 05 Nov 2009
Type Conference
Year 2008
Where ICPR
Authors C. Lee Giles, Yang Song
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