# Deep Learning and Embedding Based Latent Factor Model for Collaborative Recommender Systems
## 5 Experiments
### Experimental Results  
Table 2 lists MAE and RMSE metrics used in the experiments. The results in Table 2 are the averages for five different pieces of training, along with marginal error estimates for each metric and a 95% confidence interval. Our analysis of the results shows that their coefficient of variance is less than 1%. These findings indicated that outcomes are reproducible and that the proposed approach generates reproducible results. The performance result of our proposed method, DELCR, is summarized in Table 3. The bold result was achieved using our method. When compared to the methods depicted in Table 4, the percentage improvement result shows a significantly improved performance overall. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Folds**} & \multicolumn{2}{c}{**For ML-100K**} & \multicolumn{2}{c}{**For ML-1M**} \\ \cline{2-5}  & **MAE** & **RMSE** & **MAE** & **RMSE** \\ \hline Fold 1 & 0.672 \(\pm\) 0.005 & 0.855 \(\pm\) 0.005 & 0.643 \(\pm\) 0.007 & 0.818 \(\pm\) 0.004 \\ Fold 2 & 0.674 \(\pm\) 0.003 & 0.857 \(\pm\) 0.007 & 0.642 \(\pm\) 0.005 & 0.817 \(\pm\) 0.006 \\ Fold 3 & 0.671 \(\pm\) 0.004 & 0.853 \(\pm\) 0.002 & 0.644 \(\pm\) 0.003 & 0.817 \(\pm\) 0.001 \\ Fold 4 & 0.673 \(\pm\) 0.007 & 0.855 \(\pm\) 0.007 & 0.642 \(\pm\) 0.001 & 0.818 \(\pm\) 0.004 \\ Fold 5 & 0.675 \(\pm\) 0.007 & 0.858 \(\pm\) 0.003 & 0.641 \(\pm\) 0.003 & 0.818 \(\pm\) 0.003 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the DELCR from five-fold validation for both datasets. The values show the metrics’ average and a 95% confidence interval in a five-fold validation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**For ML-100K**} & \multicolumn{2}{c}{**For ML-1M**} \\ \cline{2-5}  & **MAE** & **RMSE** & **MAE** & **RMSE** \\ \hline MDA & 0.758 & 0.981 & 0.686 & 0.879 \\ CDL & 0.742 & 0.953 & 0.689 & 0.871 \\ RMF & 0.732 & 0.938 & 0.689 & 0.876 \\ DMF & 0.735 & 0.940 & 0.691 & 0.878 \\ HLFM & 0.750 & 0.962 & 0.698 & 0.880 \\ DLFCF & 0.717 & 0.901 & 0.678 & 0.854 \\
**DELCR** & **0.674** & **0.856** & **0.643** & **0.818** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison result of DELCR for both datasets. The bold result is obtained using the proposed method. According to Table 4, the proposed method, DELCR, outperforms all other methods for both datasets. Particularly when contrasted to our highly motivated DMF model, our DELCR approach with ML-100K results in a relative improvement of 8.30% of MAE and 8.94% of RMSE. In addition, the method delivers a relative improvement with MAE of 6.95% and RMSE of 6.83% for the ML-1M dataset. Furthermore, when contrasted to the DL-based models, CDL and MDA, the advances made by our model are significant. For these two particular models, there is an average relative improvement of 10.12% of MAE and 11.46% of RMSE for the ML-100K dataset whereas, for ML-1M dataset, there is an average relative improvement of 6.48% of MAE and 6.51% of RMSE. The reasons for this best performance achievement are: (i) the use of embedded ratings will effectively alleviate the problem of the sparse dataset and (ii) the separate mapping of these dense embedded vectors to our proposed architecture helps to discover the complex and abstract latent features of these two different entities called users and items effectively. * Once more, the discrepancy between the DELCR and RMF methods is significant. When we see the comparison with the ML-100K dataset, we got a 7.92% and 8.74% MAE and RMSE relative improvement, respectively. For the ML-1M dataset, the method gives a relative improvement with 6.68% of MAE and 6.62% of RMSE. This means that using a deep learning architecture allows us to successfully extract the latent variables in our data, which was previously the fundamental constraint of most classic matrix factorization-based models.