## 4 Implementation and Experiment Results
### Recommendation Illustration  
Typically, textual descriptions contain many trivial and meaningless words. The preprocessing tasks, including tokenization, stop words removing, and stemming, are performed to generate a set of valid terms from textual descriptions. After text preprocessing, 16,444 terms are obtained. Based on the set of terms, every movie is represented as \(<\)\(v_{i,1}\), \(v_{i,2}\), \(\ldots\), \(v_{i,1644}\)\(>\) where \(v_{ij}\) is the occurrence frequency of term \(j\) in movie \(i\). The term vectors with 16,444 dimensions cause the sparsity problem and result in long item similarity evaluation time. Therefore, the DAE is applied to extract compact content features from high-dimensional vectors. To improve the performance of the DAE, one hidden layer is added between layers \(\widetilde{\mathbf{x}}\) and \(\mathbf{y}\), and one hidden layer is added between layers \(\mathbf{y}\) and \(\mathbf{z}\). Table 1 shows the settings of topological structure and training parameters of the DAE. Besides, the dimensionality of reduced content features (the numbers of nodes in \(\mathbf{y}\)) is suggested as 164 after a set of experiments. The effect of the DAE settings will be further discussed in Section 4.3.  
For illustration purposes, we first define a movie receiving no greater than four rating (i.e., the given threshold value \(\rho=4\)) as a CS movie. In this case, 20 items such as movie ID 549, 617, 684, 990, and 1007 are considered the CS items, while the rest of the movies are considered non-CS movies. [PERSON]'s correlation coefficient in Equation (3) is applied to evaluate the similarity between each pair of a CS movie and a non-CS movie. Based on the ratings of \(\alpha\) similar non-CS items, the CS items' ratings for each user can be generated using Equation (4). Table 2 shows the predicted ratings of the five example CS movies for all users when \(\alpha\) is set as 30. For example, the predicted ratings for user seven and movie ID 549, 617, 684, 990, 1007 are 4, 5, 4, 3, and 4, respectively. Note that \"-\" in the table indicates \"no predicted rating\" since no similar non-CS movies can be found when applying Equation (4). The effect of \(\alpha\) value on the recommendation result will be further discussed in Section 4.3.  
Next, the NNCF predictor is built based on the updated preference matrix \(\mathbf{R}^{\prime}\). Since one-hot encoding is applied in the NNCF predictor, the user vector and movie vector's input dimension are 87,122 and 12,747, respectively. An entry in both user vector and movie vector is projected to latent vectors with 32 dimensions in the embedding layer. A two hidden layer structure in the MLP is applied where the Relu activation function is used. The settings of the topological structure and parameters for the NNCF predictor are shown in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of nodes for \(\mathbf{x}\) and \(\mathbf{z}\) & 16,444 \\ Numbers of nodes in the two hidden layers & (987, 987) \\ Number of nodes for \(\mathbf{y}\) & 164 \\ Batch size & 10 \\ Loss function & cross entropy \\ \hline \hline \end{tabular}
\end{table}
Table 1: The structure and parameter settings for the DAE.  
The well-trained NCCF predictor is then used to generate predicted ratings for all movies when a target user is requested. The movies with higher ratings are then feedbacked to the user. Table 4 shows that the top 50 ranked movies are suggested for User ID 6.