# A Two-Phase Deep Learning-Based Recommender System: Enhanced by a Data Quality Inspector
## 4 Results
### Deep Learning-Based Product Recommender
#### 4.3.3 Recommenders Comparison

Table 5: Architecture of NNMF recommender engine. As mentioned before, the F1 score is calculated by using precision and recall as inputs. Since recall is based on the list of relevant items to users within a dataset, it is important to clarify that the size of this list is different for each dataset. For example, \(DATA_{init\_-all}\) possesses 240,000 items (40% of 600,000) that are relevant to users, \(DATA_{pruned\_LSTMS}\) has 205,126 items (38% of 547,032) that must be recommended to users, and \(DATA_{pruned\_LST2}\) holds 180,981 items (36% of 505,269) that are relevant to users. The performance trend per indicator for every method, produced when using LSTM-5 or BERT-2 against the results obtained with \(DATA_{init\_-all}\), is clarified from Figures 9-12. For instance, as shown in Figure 9, SVD++ improves RMSE by 3.09% and 2.87% when executing BERT-2 and LSTM-5, respectively. The highest decrease is reported in DLbR, reducing RMSE by 9.60% and 6.25% when using \(DATA_{pruned\_BERT2}\) and \(DATA_{pruned\_LSTMS}\), respectively. Lastly, although SVD stated the lowest decrease, it still experienced satisfactory enhancements when using the two quality inspectors. Similarly, Figure 10 reports that DLbR enhances the F1 score by 5.30% and 3.89% when exploiting BERT-2 and LSTM-5, respectively. The lowest boost is generated by NNMF, which improves the F1 score by 0.14% when using \(DATA_{pruned\_BERT2}\) and 0.23% when utilizing \(DATA_{pruned\_LSTMS}\). Regarding SVD++ and SVD, both experienced a regular F1 score increase between 1.13% and 3.03% when applying BERT-2 or LSTM-5 quality inspectors. Likewise, Figure 11 proves the positive impact on accuracy when applying a quality inspector, where all the models, except for NNMF, reported a steady accuracy boost between 1.74% and 4.04%. Finally, as shown in Figure 12, total running time is the indicator that encountered the best enhancements, as the four models report a total running time reduction between 6.60% and 23.23% when using a \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **NNMF** & **DLbR** & **SVD** & **SVD++** \\ \hline \multirow{4}{*}{DATAinit\_-all} & RMSE & 0.294 & 0.962 & 0.814 & 0.920 \\  & F1 score & 98.48\% & 83.07\% & 50.42\% & 60.67\% \\  & accuracy & 98.79\% & 84.69\% & 73.38\% & 76.15\% \\  & time (min) & 26.44 & 24.72 & 4.22 & 6.82 \\ \hline \multirow{4}{*}{DATApruned LSTM5} & RMSE & 0.287 & 0.902 & 0.801 & 0.894 \\  & F1 score & 98.71\% & 86.95\% & 52.35\% & 61.81\% \\  & accuracy & 98.82\% & 87.17\% & 75.57\% & 77.90\% \\  & time (min) & 23.71 & 23.09 & 3.79 & 5.94 \\ \hline \multirow{4}{*}{DATApruned BERT2} & RMSE & 0.284 & 0.869 & 0.805 & 0.892 \\  & F1 score & 98.62\% & 88.37\% & 53.45\% & 63.00\% \\ \cline{1-1}  & accuracy & 98.75\% & 88.73\% & 77.00\% & 79.28\% \\ \cline{1-1}  & time (min) & 22.19 & 21.13 & 3.48 & 5.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results achieved per each model with three datasets generated from Movies and TV. Figure 9: Comparison of the variations produced when using LSTM-5 or BERT-2 against the dataset \(DATA_{init\_-all}\), of the four models in the RMSE indicator. Figure 13 shows the average percent variations experienced by the four methods, per indicator, and for every quality inspector. For example, all recommenders averaged an accuracy increase of 2.69% when applying BERT- 2, but an increase of 1.61% when using \(DATA_{\textit{printed LSTM5}}\). BERT- 2 outperforms the results of LSTM- 5 in all the four indicators, and thus, it is considered the best quality inspector. Figure 11: Comparison of the variations produced when using LSTM-5 or BERT-2 against the dataset \(DATA_{\textit{init. }-\textit{all}}\), of the four models in accuracy indicator. Figure 12: Comparison of the variations produced when using LSTM-5 or BERT-2 against the dataset \(DATA_{\textit{init. }-\textit{all}}\), of the four models in total running time. Figure 10: Comparison of the variations produced when using LSTM-5 or BERT-2 against the dataset \(DATA_{\textit{init.