Instructions to use Subhadip007/UERP_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Subhadip007/UERP_Model with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Subhadip007/UERP_Model") - Notebooks
- Google Colab
- Kaggle
UERP_Model Changelog
svd_model_25m.pkl (current, recommended)
- Trained on MovieLens 25M ratings (25,000,095 ratings, 162,541 users, 59,047 movies)
- SVD, n_factors=50, n_epochs=20
- RMSE: 0.7728 | MAE: 0.5832 (held-out 10% test split)
- Serving: min_rating_count=100 filter applied
svd_model_v1.pkl (deprecated, kept for comparison)
- Trained on MovieLens-latest-small (100,836 ratings)
- RMSE: 0.8775 | MAE: 0.6742
ncf_model_v2.keras (Stage 4 — Neural Collaborative Filtering)
- Trained on MovieLens 25M, embedding_dim=64, 3-layer MLP (256-128-64)
- RMSE: 0.8013 | MAE: 0.6030 (original 0.5-5 scale)
- Comparison: underperforms SVD baseline (RMSE 0.7728) even after tuning attempt (32-dim -> 64-dim)
- Finding consistent with Rendle et al. (RecSys 2020), "Neural Collaborative Filtering vs. Matrix Factorization Revisited" — well-tuned MF/dot-product often outperforms learned-MLP similarity for pure rating-prediction tasks.
- Decision: SVD remains primary CF signal for MVP serving. NCF kept as a documented experiment / potential weak-signal contributor for future Stage 6 ranking ensemble.