## 5 Conclusions  
Recommendation systems are now playing an essential role in many online applications. Companies such as Amazon, Google, and Netflix have massively applied the technique to their services by estimating their potential customers' preferences. Although many recommendation methods have been proposed recently, most previous researches suffer from the cold start (CS) problem [41]. Only a small number of ratings are available for some items. To solve the difficulties, this research develops a two-stage CS item recommendation system. The system includes two major components, which are the DACR  
Figure 8: The performance of the proposed recommender for two Amazon datasets under different DACR settings. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**DatasetFeatures** & **Amazon All Beauty** & **Amazon CDs \& Vinyl** \\ \hline No. of Users & 324,038 & 1,944,316 \\ No. of Items & 32,586 & 434,060 \\ No. of Ratings & 371,345 & 4,543,369 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The main features for the two Amazon datasets. generator and the NNCF predictor. In the DACR generator, the textual descriptions of items are adopted as auxiliary information for generating the content features of items. Next, a neural network-based dimension reduction method, denoising autoencoder (DAE), is applied to extract content features from vectors. DAE can compress the vectors effectively while maintaining the characteristics of the original vectors. Moreover, it can significantly reduce the computational time for item similarity evaluations. Thus, the CS items' ratings are efficiently derived based on the ratings of similar non-CS items. In the second stage, the NNCF predictor is used to predict the ratings in the updated user-item preference matrix after adding CS items' ratings. In the NCCF predictor, the long sparse vectors are projected to a dense latent vector in the embedding layer as latent vectors. Next, latent vectors are fed into MLP layers for user-item interaction learning. When the user ID is specified, the trained NNCF predictor will return the ratings of all items. A set of experiments shows that the DAE can significantly reduce the computational time for item similarity evaluations while keeping the original features' characteristics. Besides, the experiments indicate that the proposed NNCF predictor outperforms several popular baseline algorithms. Finally, we demonstrate that the proposed CS item recommender can achieve up to 8% MAE improvement compared to adding no CS item rating. Although the proposed system is efficient in solving the CS item recommendation problem, some possible directions can be further improved. First, this study adopted the textual descriptions of items as auxiliary information to derive CS items' ratings. It is worthwhile to apply different content information such as images and videos to derive items' content features. Second, although the performance of vector dimension reduction done by the DAE is significant, different types of DAE can be tested. Third, overfitting might appear in the proposed NNCF recommender. Further study can try different strategies such as dropout and L1/L2 regularization to avoid the overfitting difficulties. Finally, it might be interesting to apply the proposed system to variant applications such as online music and news. Conceptualization, C.-Y.T. ; Formal analysis, Y.-F.C. ; Methodology, C.-Y.T. ; Software, Y.-F.C. ; Validation, Y.-F.C. ; Visualization, Y.-J.C.; Writing-review & editing, Y.-J.C. All authors have read and agreed to the published version of the manuscript. This work was partially supported by the Ministry of Science and Technology of Taiwan, R.O.C., No. 105-2221-E-155-024-MY3. Not applicable. The data presented in this study are available online at [[https://www.kaggle.com/netflix-inc/netflix-prize-data](https://www.kaggle.com/netflix-inc/netflix-prize-data)]([https://www.kaggle.com/netflix-inc/netflix-prize-data](https://www.kaggle.com/netflix-inc/netflix-prize-data)). The authors declare no conflict of interest.