# TRSDL: Tag-Aware Recommender System Based on Deep Learning-Intelligent Computing Systems
## 4 Experiments
### Experimental Results and Discussions
#### 4.3.2 Comparative Results and Evaluations
Accordingly, not only users' preferences and opinions towards items are implicitly discovered, but also the problem of redundancy and ambiguity caused by user-defined tags is mitigated. On the other hand, TRSDL captures more general and abstract hidden features through deep networks. It enables complex and high-level relationships within data to be represented in deeper layers of the networks, and then effectively learns non-linear relationships among users, items and tags. The results show that TRSDL outperforms two state-of-the-art algorithms, BiasedMF and I-AutoRec, proving that our proposed model is a competitive method of rating prediction studies. We also note that the performance of I-AutoRec is dramatically worse than BiasedMF, while I-AutoRec outperforms BiasedMF when it was proposed in [35]. We guess the phenomenon is related to the sparsity of the dataset. I-AutoRec works well on the MovieLens-1 m and MovieLens-10 m datasets. The sparsity of the two datasets is 95.754% and 98.692%, respectively, and the sparsity of the data we used (after preprocessing) reaches 99.551%. This also proves from the side that TRSDL is robust to the sparse data to some degree. DNN and BOW-TRSDL models are similar to TRSDL. The difference between DNN and TRSDL is the type of networks in the user model (i.e., DNN uses a three-layer neural network to replace the three-layer LSTM of TRSDL), while the difference between BOW-TRSDL and TRSDL is the approach of tags representation (i.e., BOW-TRSDL represents tags based on the bag-of-words model instead of word embeddings). TRSDL performs better than DNN (the \(MAE\) and \(RMSE\) decreased by 0.077 and 0.141, respectively), which verifies that learning the temporal features of users through the recurrent neural networks provides useful information for improving the recommendations. TRSDL outperforms BOW-TRSDL, indicating that the bag-of-words model cannot discriminate redundant tags with similar \begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & \(MAE\) & \(RMSE\) \\ \hline
**ItemCF** & 1.091 & 1.329 \\
**UserCF** & 1.237 & 1.481 \\
**TAG-CF** & 1.042 & 1.366 \\
**BiasedMF** & 0.673 & 0.904 \\
**I-AutoRec** & 0.703 & 1.057 \\
**DNN** & 0.735 & 1.011 \\
**BOW-TRSDL** & 0.736 & 0.971 \\
**TRSDL** & **0.658** & **0.870** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The comparison of \(MAE\) and \(RMSE\) values for the dataset. meanings or ambiguous tags with multiple meanings. In addition, the vector generated by the BOW model is very high dimensional which leads to huge resource consumption. We analyze the variance and the two-tailed \(t\)-test between the performances of TRSDL and the three best baselines (BiasedMF, I-AutoRec and DNN) to verify whether the performance differences are statistically significant. Firstly, we analyze the statistical differences of \(MAE\) measurement. The _sig_ value of [PERSON]'s test is 0.126 > 0.05, indicating that the variance is homogenous. After that, we conduct the one-way analysis of variance for all groups. The \(p\)-value is 8.21\(\times\) 10\({}^{-14}\), which means the difference is significant. Furthermore, we perform \(t\)-tests for TRSDL and baselines. The \(p\)-value of TRSDL-BaisedMF, TRSDL-I-AutoRec and TRSDL-DNN are 3.27 \(\times\) 10\({}^{-6}\), 2.73 \(\times\) 10\({}^{-13}\) and 2.70 \(\times\) 10\({}^{-8}\), respectively. All \(p\)-values are smaller than 0.05, illustrating the \(MAE\) differences are statically significant. \(p\)-values can verify whether the effect exists, however, they do not reveal the size of the effect. To overcome the limitation of \(p\)-values, we also report the effect size with the Cohen's d values [56]. The \(d\) values are 3.28, 10.22 and 4.72, respectively, all effect sizes are bigger than 0.8 indicating big effects. Secondly, the analysis of \(RMSE\) is similar. The _sig_ value of [PERSON]'s test is 0.166 indicating the homogeneity of variance. The \(p\)-value from one-way variance analysis is 1.65 \(\times\) 10\({}^{-29}\), which means the difference reaches the significant level.