## 6 Novel Initialization Method Using Content-Based Information for Matrix Factorization Techniques
At the same time, SVD-genome and NMF-genome models totally outperform their corresponding counterparts from 2.44% to 5.72% in RMSE. Once again, it can be seen that initializing latent vectors with 600 scores produced by an HCAE (highlighted rows) provides the lowest error rate when compared with others representations, which further proves the superiority of the HCAE against AE variants stated in the previous section. These remarkable improvements prove that learning the user and item latent features from _appropriate_ initial vectors could greatly benefit matrix factorization models: it not only helps the training process to converge faster but also enhances the accuracy. In real-life applications, the word _\"appropriate\"_ here implies that we could take advantage of all accessible knowledge including user preferences and available metadata, not limited to user-contributed genome tags in the above experiments, in order to characterize user and item features as precisely as possible. This empirical result suggests that the more meaningful values of initial latent vectors, the more likely it is to achieve better matrix factorization-based RSs in terms of precision and computational complexity. Another advantage of the newly proposed method is that if adopting the original 1128 genome tags or the shortened 1044 ones preprocessed by NLP for initialization, movie latent vectors generated after training have much better interpretability than the ones from traditional matrix factorization models. As demonstrated in Figure 5, the most relevant \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **RMSE** & **Epochs** & **Time [s]** \\ \hline NMF (\(k=1128\)) & 0.7993 & 305 & 32,115 \\ \hline \(\mathit{NMF}\)-_genome_ & _0.7797_ & _200_ & _21,220_ \\ _(1128 original scores)_ & (\(-2.45\%\)) & (\(-34.43\%\)) & (\(-33.92\%\)) \\ \hline NMF (\(k=1044\)) & 0.7987 & 290 & 18,802 \\ \hline \(\mathit{NMF}\)-_genome_ & _0.7792_ & _165_ & _10,843_ \\ _(1044 NLP-preprocessed scores)_ & (\(-2.44\%\)) & (\(-43.10\%\)) & (\(-42.33\%\)) \\ \hline NMF (\(k=600\)) & 0.7984 & 265 & 9,937 \\ \hline \(\mathit{NMF}\)-_genome_ & _0.7718_ & _125_ & _4695_ \\ _(600 scores of DAE)_ & (\(-3.33\%\)) & (\(-52.83\%\)) & (\(-52.75\%\)) \\ \hline \(\mathit{NMF}\)**-_genome_ & _0.7688_ & **97** & _3729_ \\ _(600 scores of HCAE)_ & (\(-3.71\%\)) & (\(-63.40\%\)) & (\(-62.47\%\))_ \\ \hline SVD (\(k=1128\)) & 0.7930 & 230 & 11,201 \\ \hline \(\mathit{SVD}\)-_genome_ & _0.7582_ & _55_ & _2713_ \\ _(1128 original scores)_ & (\(-4.39\%\)) & (\(-76.09\%\)) & (\(-75.78\%\)) \\ \hline SVD (\(k=1044\)) & 0.7927 & 150 & 5895 \\ \hline \(\mathit{SVD}\)-_genome_ & _0.7547_ & _45_ & _1787_ \\ _(1044 NLP-preprocessed scores)_ & (\(-4.79\%\)) & (\(-70.00\%\)) & (\(-69.69\%\)) \\ \hline SVD (\(k=600\)) & 0.7925 & 140 & 3018 \\ \hline \(\mathit{SVD}\)-_genome_ & _0.7536_ & 35 & 905 \\ _(600 scores of DAE)_ & (\(-4.91\%\)) & (\(-75.00\%\)) & (\(-70.01\%\)) \\ \hline \(\mathit{SVD}\)**-_genome_ & _0.7472_ & _30_ & _814_ \\ _(600 scores of HCAE)_ & (\(-5.72\%\))_ & (\(-78.57\%\))_ & (\(-73.03\%\))_ \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison between randomly initialized matrix factorization models and their custom initialized counterparts integrating user and movie feature vectors. tags of _Titanic_ (1997) deduced from the learned movie features are highly reasonable for both versions of the genome tags. For user latent vectors, there are clear differences before and after training (user with \(id=1\) for example). However, it is reasonable to expect that learned user vectors could provide a better understanding of user preferences and can be employed for further analysis owing to their significant contribution towards generating RSs with higher accuracy. The two most accurate models, NMF-genome and SVD-genome utilizing 600 HCAE-generated scores, are chosen to compare with the baselines listed in Section 4.3.