# TD-DNN: A Time Decay-Based Deep Neural Network for Recommendation System
## 6 Conclusions and Future Scope  
In this paper, we propose a novel Time Decay-based Deep Neural Network (TD-DNN), which takes the preprocessed data at the input layer of DNN. In the data preprocessing step, firstly, the noisy ratings from the dataset are detected and corrected using the Matrix Factorization approach, and then, a power function is applied to emphasize the most recent ratings. The neural network model learns these non-noisy time weighted ratings and feeds them into MLP to produce predicted ratings as output. Extensive experimentation on real-world datasets demonstrates that our model achieves better outcomes than all other models. Our method may greatly increase the accuracy of movie recommendations when compared to existing collaborative filtering techniques.  
In the future research, it is planned to extend another Deep Learning approaches such as encoder, decoder to improve the performance and solve the data sparsity and cold start problems. It is also planned to include the advanced tuning parameters with the other  
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Dataset** & **Function** & **20** & **60** & **100** & **150** & **200** \\ \hline \multirow{8}{*}{ML-100k} & Concave & 0.5109 & 0.5096 & 0.5101 & 0.5110 & 0.5120 \\  & Convex & 0.5866 & 0.5869 & 0.5882 & 0.5901 & 0.5911 \\  & Exponential & 0.5973 & 0.5985 & 0.6004 & 0.6021 & 0.6033 \\  & Linear & 0.4785 & 0.4781 & 0.4785 & 0.4788 & 0.4794 \\  & Logistic & 0.4662 & 0.4661 & 0.4664 & 0.4674 & 0.4680 \\  & Power & 0.4261 & 0.4249 & 0.4256 & 0.4261 & 0.4270 \\ \hline \hline \end{tabular}
\end{table}
Table 11: MAE Results of Time-Decay Function.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**ML-100k**} & \multicolumn{2}{c}{**ML-1M**} & \multicolumn{2}{c}{**Epinions**} \\ \cline{2-7}  & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** \\ \hline IBCF & 0.7897 & 1.0687 & 0.7795 & 1.0564 & 1.0075 & 1.3745 \\ UBCF & 0.7429 & 1.0240 & 0.7455 & 1.0245 & 0.9853 & 1.3510 \\ SVD & 0.7515 & 1.0024 & 0.7215 & 0.9295 & 0.8935 & 1.3365 \\ DLCRS & 0.7421 & 0.9993 & 0.7125 & 0.9112 & 0.8844 & 1.3122 \\ Deep EDU & 0.6725 & 0.8932 & 0.6571 & 0.8794 & 0.7634 & 0.9891 \\ NNBN & 0.7206 & 0.9134 & 0.6987 & 0.8858 & 0.8025 & 1.2452 \\ NCF & 0.6513 & 0.8710 & 0.6338 & 0.8532 & 0.7621 & 0.9835 \\ NMF & 0.7321 & 0.9916 & 0.7260 & 1.0362 & 0.8234 & 1.2912 \\
**TD-DNN (Proposed)** & **0.6275** & **0.8312** & **0.6196** & **0.8284** & **0.7364** & **0.9638** \\ \hline \hline \end{tabular}
\end{table}
Table 12: MAE and RMSE values of various models.  
contextual data into the movie recommendation in future work. Apart from the time and contextual data, trust is also an important parameter that helps in improving the accuracy of RS; therefore, it will be considered in the development of RS.  
Conceptualization, G.J. and T.M.; methodology, G.J. and T.M.; software, G.J.; validation, T.M. and S.C.S.; formal analysis, G.J.; investigation, T.M., S.C.S., S.A., H.K.; resources, G.J.; data curation, G.J.; writing--original draft preparation, G.J.; writing--review and editing, T.M., S.C.S., S.A. and H.K.; visualization, G.J. and S.A.; supervision, T.M., S.C.S. and H.K.; project administration, H.K.; funding acquisition, H.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B04032598).  
Not applicable.  
The datasets used in this paper are publically available and their links are provided in the reference section.  
The authors declare no conflict of interest.