Table 7.
Comparison our model with literature.
| Ref. No | Year | Models | Dataset | RMSE | MAE | Precision | Recall | Hybrid model | Training time (hours) | Memory usage (GB) |
|---|---|---|---|---|---|---|---|---|---|---|
| 30 | 2020 | CF for RS | Movielens 100 k | 0.917 | – | – | – | No | – | – |
| 25 | 2021 | CF based SVD and RBM | Movielens 100 k | 0.9557 | 0.6699 | – | – | Yes | – | – |
| 10 | 2022 | SVD | Movielens 100 k | 0.9071 | 0.7159 | – | – | No | – | – |
| 13 | 2023 | Matrix Factorization | Movielens 100 k | 0.9392 | – | – | – | No | – | – |
| 14 | 2023 | CF, SVD, DL | Movielens 100 k | 0.9908 | – | – | – | No | – | – |
| 15 | 2023 | CF | Movielens 100 k | 0.9119 | 0.7084 | – | – | No | – | – |
| 31 | 2024 | Hybrid CNN | Movielens 100 k | 0.889 | 0.677 | – | – | Yes | – | – |
| Our model | 2024 | Hybrid CF, NCF CBF, RNN | Movielens 100 k | 0.7723 | 0.6018 | 0.8127 | 0.7312 | Yes | 1.6 | 8 |