# Multi-Feature Extension via Semi-Autoencoder for Personalized Recommendation
## 5 Experiments
### Experimental Results  
For each dataset, a sample of 50%, 60%, 70%, 80%, and 90% is utilized for training, while the remainder is used for testing. The experimental results of MAE and RMSE on two MovieLens datasets are presented in Tables 2-4 and Figures 4 and 5. With different numbers of training samples, the values in bold in the Tables indicate the best among all methods and the following observations can be drawn from the experimental results:  
* The histograms in Figures 4 and 5 reflect that the RMSE and MAE values of all methods decrease periodically as training data increases, indicating that the performance of all methods improves as training data increases. * Traditional models based on MF (such as SVD++) perform poorly due to data sparsity. Compared with this method, deep neural network methods (such as I-AutoRec, HCRSA, PRKG, and MFSAE) can achieve better performance, showing deep neural networks' powerful ability to learn personalized feature representations. * In the autoencoder-based methods, the performance of MFSAE is better than the I-AutoRec, HCRSA, and PRKG models. The HCRSA method solves the data sparsity problem on top of I-AutoRec by introducing additional attribute information and the PRKG adds side information by introducing additional features via a KG. However, MFSAE introduces external extended feature information via DBpedia and uses LSI model to learn hidden relations. This shows the superiority of effective side information in improving personalized recommendation performance and the advantage of feature representation learning. * Moreover, GraphRec achieves excellent recommendation performance on the MovieLens dataset due to the introduction of item and user graph features. GraphRec performs better than MFSAE when the training rate is 0.5 or 0.6, which means that the graph feature representation is more efficient when the data is sparse. Moreover, MFSAE outperforms GraphRec when the training data is large, reflecting the strong advantage of deep learning feature representations. Notably, the model structure of MFSAE is simpler. * Typically, data sparsity has been an essential factor for recommendation performance. As shown in Table 1, MovieTweetings 10 K, with tiny rating data, is the sparsest and MovieLens 1 M is more sparse than MovieLens 100 K. From the results, we can conclude that the performance of RS methods, especially deep learning models, will be considerably affected by the amount of training data. * Overall, MFSAE performs better than most methods. Due to its simple structure for learning the latent features of the side information introduced by DBpedia through LSI model, MFSAE achieves stable and better performance. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multirow{2}{*}{**Methods**} & \multicolumn{6}{c}{**Proportion of Training Data**} \\ \cline{3-6}  & & **50\%** & **60\%** & **70\%** & **80\%** & **90\%** \\ \hline \multirow{6}{*}{MAE} & SVD++ & 0.683 & 0.678 & 0.674 & 0.668 & 0.666 \\  & I-AutoRec & 0.729 & 0.723 & 0.714 & 0.704 & 0.698 \\  & HCRSA & 0.692 & 0.687 & 0.681 & 0.675 & 0.668 \\  & GraphRec & **0.683** & 0.679 & 0.673 & 0.668 & 0.664 \\  & PRKG & 0.705 & 0.696 & 0.690 & 0.684 & 0.679 \\  & MFSAE & 0.684 & **0.677** & **0.672** & **0.667** & **0.662** \\ \hline \multirow{6}{*}{RMSE} & SVD++ & 0.879 & 0.866 & 0.859 & 0.851 & 0.848 \\  & I-AutoRec & 0.914 & 0.905 & 0.896 & 0.888 & 0.881 \\ \cline{1-1}  & HCRSA & 0.892 & 0.885 & 0.879 & 0.871 & 0.863 \\ \cline{1-1}  & GraphRec & **0.870** & 0.866 & 0.857 & 0.851 & 0.847 \\ \cline{1-1}  & PRKG & 0.898 & 0.888 & 0.881 & 0.874 & 0.868 \\ \cline{1-1}  & MFSAE & 0.872 & **0.863** & **0.857** & **0.851** & **0.847** \\ \hline \hline \end{tabular}  
* Tip: The bolder ones mean better.  
\end{table} 
Table 4: The performance of MAE and RMSE on MovieLens 1 M dataset.