## 7 Conclusions  
In this work, a novel architecture of autoencoder named HCAE is first proposed in order to discover essential information from the original Tag Genome of each movie. By integrating a 1D-CNN into the encoder part, an HCAE proves its capability in feature extraction compared to a vanilla AE and its variants, especially without the need for the time-consuming data preprocessing stage. Secondly, a new method of profiling users is introduced using the HCAE-generated movie feature vectors and rating information. The new representations of users and movies are then utilized as the initial latent vectors during the training stage of common matrix factorization techniques. In addition to learning movie feature vectors with better explainability, experimental results demonstrate that the custom-initialized SVD/NMF models not only converge much faster in the training but also outperform the randomly initialized counterparts in both rating prediction and ranking tasks. It is thus expected that matrix factorization-based RSs could be greatly improved via generating robust user and item profiles from all available information for initialization.  
The proposed HCAE has enabled several potential ideas on applying state-of-the-art feature extractors such as a deep CNN or a transformer-based AE to generate robust hidden feature vectors. Another potential direction is to adopt convolutional or transformer-based models not only as an AE, but as an end-to-end recommendation model.  
Conceptualization, T.N.D., N.N.D. and T.G.D.; methodology, M.H.T.; software, Q.H.D.; validation, T.N.D., D.M.N. and M.H.T.; formal analysis, T.N.D.; investigation, T.N.D., N.N.D. and T.G.D.; resources, T.N.D.; data curation, T.N.D. and N.N.D.; writing--original draft preparation, N.N.D. and T.G.D.; writing--review and editing, T.N.D. and Q.H.D.; visualization, T.G.D.; funding acquisition, D.M.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Project of Hanoi University of Science and Technology under Grant no. T2020-PC-024.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **RMSE** & **P@5** & **P@10** & **R@5** & **R@10** \\ \hline ii-CF & 0.8046 & 0.7967 & 0.7721 & 0.4261 & 0.5541 \\  & (\(-\)7.13\%) & (+4.06\%) & (+4.45\%) & (+3.79\%) & (+3.20\%) \\ I-AutoRec & 0.7808 & 0.7778 & 0.7559 & 0.3972 & 0.5228 \\  & (\(-\)4.30\%) & (+6.33\%) & (+6.46\%) & (+10.32\%) & (+8.67\%) \\ NMF & 0.7981 & 0.7951 & 0.7743 & 0.4296 & 0.5583 \\  & (\(-\)6.38\%) & (+4.25\%) & (+4.18\%) & (+3.00\%) & (+2.46\%) \\ SVD & 0.7922 & 0.8005 & 0.7786 & 0.4322 & 0.5628 \\  & (\(-\)5.68\%) & (+3.60\%) & (+3.65\%) & (+2.42\%) & (+1.68\%) \\ SVD++ & 0.7894 & 0.8030 & 0.7817 & 0.4339 & 0.5639 \\  & (\(-\)5.35\%) & (+3.30\%) & (+3.27\%) & (+2.03\%) & (+1.48\%) \\ \hline kNN-Content\({}^{\text{HCAE}}\)-SVD & 0.7559 & 0.8226 & 0.8007 & 0.4379 & 0.5619 \\  & (\(-\)1.15\%) & (+0.94\%) & (+0.92\%) & (+1.13\%) & (+1.83\%) \\  & 0.7516 & 0.8250 & 0.8031 & 0.4397 & 0.5648 \\ kNN-Content\({}^{\text{HCAE}}\)-SVD++ & (\(-\)0.59\%) & (+0.65\%) & (+0.62\%) & (+0.72\%) & (+1.33\%) \\ \hline NMF-genome & 0.7688 & 0.8174 & 0.7941 & 0.4389 & 0.5689 \\  & (\(-\)2.81\%) & (+1.57\%) & (+1.73\%) & (+0.90\%) & (+0.61\%) \\
**SVD-genome** & **0.7472** & **0.8304** & **0.8081** & **0.4429** & **0.5724** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison between the proposed models utilizing 600 HCAE-generated feature vectors and the baseline models.