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MovieLens :
- 10 million ratings : 72.000 users / 10.000 movies
- 20 million ratings : 138.000 users / 28.000 movies
Results
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Quadratic error
MovieLens-10M MovieLens-20M
BPMF 0.8213 0.8123
ALS-WR 0.7830 0.7746
LLORMA 0.7949 0.7843
U-CFN 0.7767 0.7663
V-CFN 0.7767 0.7663
RMSE for train/test 90/10
BPMF : rank=10
ALS-WE : rank = 200
LLORMA : rank = 20, anchor point = 30
A. Mnih and R. Salakhutdinov, “Probabilistic matrix factorization,” in Advances in neural information processing systems, 2007, pp. 1257– 1264.
B. J. Lee, S. Kim, G. Lebanon, and Y. Singerm, “Local low-rank matrix approximation,” in Proc. of ICML’13, 2013, pp. 82–90.
C. Y.Zhou,D.Wilkinson,R.Schreiber,andR.Pan,“Large-scaleparallel collaborative filtering for the netflix prize,” in Algorithmic Aspects in Information and Management. Springer, 2008, pp. 337–348.
Results
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Singular value decomposition (SVD)
Other algorithms:
●Alternating Least Square Weighted Lambda-Regularization (ALS-WR)
●Probabilistic Matrix Factorization (PMF, BPMF, NLPMF)
●Local Low Rank Matrix Approximation (LLORMA)
RUsers
Items v
u
Link with matrix factorization (optional)
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ui
V
Link with matrix factorization (optional)
activation
ri = Vui
RUsers
Items v
u
CFN computes a Non-Linear
Matrix Factorization
