# A Two-Phase Deep Learning-Based Recommender System: Enhanced by a Data Quality Inspector
## 5 Discussion  
After conducting several experiments, it can be concluded that the application of a data quality inspector, based on textual reviews, truly enhances the overall performance of a collaborative filtering recommender system. When exploiting the BERT quality inspector in two datasets, on average, it was registered a 17% decrease in the total time to run the algorithm, a 6% percentage enhancement in RMSE, and a percentage boost of between 2% and 3% in F1 score and accuracy. To assess the statistical significance of the obtained results, four recommender systems were run one time within six different data subsets: three generated from the Movies and TV data, and the other three from the Kindle Store data. The results show similar trends and comparable improvements when using the datasets pruned by LSTM5 and BERT2. The standard deviations (SD) of the percent improvements experienced by the four models when using the two subsets of \(DATA_{\textit{pruned LSTMs}}\) (produced from Movies and TV and from the Kindle Store), are an SD of 3.4% in RMSE, 1.1% in F1 score, 1.2% on accuracy, and 4.1% in total running time. Similarly, the SD of the percent improvements experienced by the four models when using the two subsets of \(DATA_{\textit{pruned BERT2}}\), are an SD of 5.9% in RMSE, 1.6% in F1 score, 1.4% on accuracy, and 5.1% in total running time.  
Since the experiments were run within two datasets, it is robust evidence to prove the importance of the data quality inspection process, data cleaning, and the use of textual  
Figure 14: Percent variations averages experienced by the four models, per indicator, and for every quality inspector with Kindle Store data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **NNMF** & **DLbR** & **SVD** & **SVD++** \\ \hline \multirow{4}{*}{DATAinit-all} & RMSE & 0.097 & 0.792 & 0.832 & 0.844 \\  & F1 score & 99.86\% & 88.58\% & 28.83\% & 37.63\% \\  & accuracy & 99.89\% & 89.80\% & 66.71\% & 69.17\% \\  & time (min) & 10.67 & 10.75 & 1.46 & 1.92 \\ \hline \multirow{4}{*}{DATApruned LSTM5} & RMSE & 0.092 & 0.695 & 0.818 & 0.827 \\  & F1 score & 99.87\% & 89.98\% & 29.91\% & 39.50\% \\  & accuracy & 99.90\% & 91.04\% & 70.00\% & 72.43\% \\  & time (min) & 0.03 & 8.58 & 1.21 & 1.69 \\ \hline \multirow{4}{*}{DATApruned BERT2} & RMSE & 0.079 & 0.703 & 0.826 & 0.834 \\  & F1 score & 99.89\% & 90.94\% & 31.60\% & 40.77\% \\ \cline{1-1}  & accuracy & 99.91\% & 91.91\% & 68.39\% & 70.94\% \\ \cline{1-1}  & time (min) & 10.00 & 8.32 & 1.15 & 1.58 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results achieved per each model with three datasets generated from Kindle Store.  
reviews as auxiliary data in the field of recommenders. Moreover, it can be determined that the five deep learning-based sentiment predictors (LSTM, Bi-LSTM, CNN, TDVE, and BERT) outperform the traditional machine learning algorithm (SVM). Similarly, it can be found that the two deep learning-based recommenders (NNMF and DLbR) exceed the performance of the two traditional machine learning methods (SVD and SVD++). Unquestionably, the deep learning approach is very suitable for these two kinds of tasks. Furthermore, comparing the results between the two deep learning-based recommenders, it is important to emphasize the leverage power of using a hybrid strategy. It was shown that NNMF, which is a mixture of one approach and two techniques, achieves better recommendation outcomes than DLbR, which is based only on MLP.