# 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  
To show the advantage of the proposed model TRSDL, we compare it with seven baselines. The baselines are: traditional algorithms based on collaborative filtering (i.e., ItemCF, UserCF and \begin{table}
\begin{tabular}{c c c} \hline \hline
**Structures** & \(MAE\) & \(RMSE\) \\ \hline NNs@1 & 0.862 & 1.104 \\ NNs@2 & 0.711 & 0.934 \\ NNs@3 & 0.694 & 0.905 \\ TRSDL@1 & 0.688 & 0.905 \\ TRSDL@2 & 0.670 & 0.881 \\ TRSDL@3 & **0.658** & **0.870** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of various model structures. \begin{table}
\begin{tabular}{c l} \hline \hline
**Structures** & **Settings** \\ \hline NNs@1 & \(lr=0.01\), \(n_{\text{H1}}=64\), \(L=20\), \(\mu=0.1\), \(\beta=0\), \(bs=256\) \\ NNs@2 & \(lr=0.01\), \(n_{\text{H1}}=128\), \(n_{\text{H2}}=64\), \(L=20\), \(\mu=0.1\), \(\beta=0\), \(bs=256\) \\ NNs@3 & \(lr=0.01\), \(n_{\text{H1}}=128\), \(n_{\text{H2}}=64\), \(n_{\text{H3}}=16\), \(L=20\), \(\mu=0.1\), \(\beta=0\), \(bs=256\) \\ TRSDL@1 & \(lr=0.01\), \(n_{\text{H1}}=64\), \(L=20\), \(\mu=0.1\), \(\beta=0\), \(bs=256\) \\ TRSDL@2 & \(lr=0.01\), \(n_{\text{H1}}=128\), \(n_{\text{H2}}=64\), \(L=20\), \(\mu=0.1\), \(\beta=0\), \(bs=256\) \\ TRSDL@3 & \(lr=0.01\), \(n_{\text{H1}}=128\), \(n_{\text{H2}}=64\), \(n_{\text{H3}}=16\), \(L=20\), \(\mu=0.1\), \(\beta=0\), \(bs=256\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters of various model structures. TAG-CF), the state-of-the-art models for rating prediction (i.e., BiasedMF and I-AutoRec), and tag-aware recommender based on deep learning (i.e., DNN and BOW-TRSDL). We first describe the competing methods as follows:  
* **ItemCF:** For every item of the test set, the weighted average ratings of the nearest items in the training set are returned. The cosine similarity is used to measure the degree of items' similarity. * **UserCF:** Similar to the itemCF model, the nearest user neighbors' weighted average ratings are regarded as predictive ratings. User similarity is measured by cosine similarity. * **TAG-CF:** This method integrates tag information to the collaborative filtering and proposes a tag-aware hybrid prediction algorithm [54]. The principle process is shown in the following steps. (1) Calculate item co-occurrence similarity \(itemSim\) based on Item-Tag matrix \(T\). (2) Establish a predictive rating matrix \(C\) based on item similarity. (3) Construct a pseudo-matrix \(M\) to integrate the rating matrix \(R\) and the predictive rating matrix \(C\). (4) Calculate user similarity \(userSim\) through \(M\),  
Table 5 depicts in detail prediction performances of TRSDL and other seven baselines on the MovieLens dataset in terms of \(MAE\) and \(RMSE\). Observing the experimental results, we have the following findings and discussions. Overall, TRSDL significantly outperforms other baselines in all metrics. The \(MAE\) and \(RMSE\) of TRSDL are 0.658 and 0.870, followed by the best baseline BiasedMF, with an \(MAE\) of 0.673 and an \(RMSE\) of 0.904. TRSDL reduced the prediction errors--\(MAE\) and \(RMSE\) decreased by 2.23% and 3.76% compared to the suboptimal BiasedMF. The superior performance of TRSDL is mainly because that integrating tag representations based on word embeddings and users' tagging behaviors within a hybrid deep learning model improves the performance. Table 5 demonstrates that, when comparing TRSDL to ItemCF, UserCF and TAG-CF models, TAG-CF and TRSDL perform better than ItemCF and UseCF in terms of indicator \(MAE\). TAG-CF and TRSDL utilize additional tag information in recommender systems, which can be regarded as the good reflections of users' preferences and items' characteristics. The reasons that TRSDL achieves much higher improvement than TAG-CF may be that: On the one hand, TRSDL uses distributed word representations to encode tags instead of discrete vectors based on the bag-of-word model so that the model takes advantage of the semantic information of tags.