Classic recommendation algorithms implementation
| DNN Model |
RMSE |
MAE |
| NeuMF |
0.9433 |
0.7485 |
| MF Model |
RMSE |
MAE |
| Baseline |
0.946 |
0.742 |
| SVD |
0.931 |
0.731 |
| SVDPlusPlus |
0.927 |
0.726 |
| Explicit ALS |
1.199 |
0.903 |
| Implicit ALS |
2.752 |
2.525 |
| Neighborhood Model |
RMSE |
MAE |
| Itemcf |
1.029 |
0.802 |
| WeightedSlopOne |
1.043 |
0.835 |
import os
from util.databuilder import DataBuilder
from algorithm.dnn.neumf import NeuMF
file_name = os.path.abspath("data/ml-100k/u.data")
data_builder = DataBuilder(file_name, just_test_one=True)
data_builder.eval(NeuMF(epochs=2), k_folds=5)
- Neural Collaborative Filtering
- Yehuda Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model
- Matrix factorization techniques for recommender systems
- Advances in Collaborative Filtering
- Slope one predictors for online rating-based collaborative filtering
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