# TRSDL: Tag-Aware Recommender System Based on Deep Learning-Intelligent Computing Systems
## 4 Experiments
### Experimental Results and Discussions
#### 4.3.1 Evaluation of Model Architectures  
To evaluate the performance of choosing different network types (LSTM or DNN) and different network depths (1, 2 and 3), we employed a bottom-up approach [53] to create different models for comparison.  
1. In the first model, we learn both users' and items' latent features from two single-layer neural networks.
2. The second one is built on Model (1)--we stack multiple hidden layers of both networks to verify whether the increase of layers can improve the model's performance.
3. We apply a single-layer LSTM in the user model instead of the DNN of Model (1).
4. In the last model, we stack multiple hidden layers based on the third model.  
For clarity, we combined Models (1) and (2) as the first group of experiments, which is represented by \"NNs\", and combined Models (3) and (4) as the second group, which is denoted by \"TRSDL\". In the two groups, network layers are distinguished by the number shown after @. For the NNs experiments, user profiles are constructed from corresponding items' average embeddings rather than from chronological item embeddings as TRSDL.  
For all baselines, we tuned the learning rate \(lr\in\{0.001,0.005,0.01,0.02,0.05,0.1\}\), the batch size \(bs\in\{128,256,512\}\), regularization strength \(\beta\in\{0,0.001,0.005,0.01\}\) and the appropriate latent dimension \(n_{h}\in\{16,32,64,128,256\}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Quantity** \\ \hline Users (\(u\)) & 7159 \\ Movies (\(i\)) & 13,396 \\ Ratings (\(r\)) & 126,083 \\ Tags (\(t\)) & 34,065 \\ Genres (\(g\)) & 19 \\ \hline \hline \end{tabular}
\end{table}
Table 2: A summary of the used dataset.  
The final hyper-parameters we used are listed in Table 3, where \(L\) denotes the max sequence length for LSTM, \(\mu\) is the balance factor for the effects of hidden concatenated features. Furthermore, to prevent gradient explosion, we apply gradient clipping to TRSDL, and the max gradient norm is set to 5.  
Experimental results of various model structures are shown in Table 4. From the table, the performances for several model architectures are quite clear and conclusive:  
1. TRSDL@3 provides the best results with an \(MAE\) of 0.658 and an \(RMSE\) of 0.870, which are 5.18% and 3.87% lower than the best results of NNs, respectively. The worst performance of TRSDL group is from the single-layer structure TRSDL@1, with an \(MAE\) of 0.688 and an \(RMSE\) of 0.905, which still outperforms all of the NNs' experiments.
2. As the number of network layers increases, the \(MAE\) value of the NNs experiment decreases from 0.862 to 0.694 (the \(RMSE\) value decreases from 1.104 to 0.905), and the \(MAE\) of the TRSDL group decreases from 0.688 to 0.658 (the \(RMSE\) decreases from 0.905 to 0.870).  
Experiment (a) verified that the proposed TRSDL significantly outperforms all NNs model architectures. As one of the most important contributions, temporal information indeed enhances the model's performance. Features learned from tagging sequential information can be used to improve predictions, and LSTM is good at processing the sequential data.  
Experiment (b) shows that the number of neural network layers plays an important role in the model behavior. In both two groups of experiments, the model based on single-layer structure is the worst performing. The performance improves when layers stack, as the lower layer captures surface characteristics of input data, while more advanced features are captured by upper layers. In other words, hidden features extracted from the shallow structure are less informative than the deep structure. Our proposed TRSDL is able to learn complex nonlinear relations within data through the deep interaction of latent features.