# A Deep Temporal Neural Music Recommendation Model Utilizing Music and User Metadata
## 4 Experiments
### Experiment Results
We could infer that 44% users listened to at least one popular song, and some of them listened to multiple popular songs. UserKNN was the second best method among all the compared methods. As UserKNN recommends items based on similar users, it does not suffer from the sparse data problem because we had enough users in the training set. Therefore, its performance was better than other baselines. The efficiency was the biggest problem of UserKNN. It needs to calculate the similarity between each user pairs and find the top users for each song. In our experiment, in which we found the top 1000 similar users for each user and recommended according to these users, the time taken for each user is about 10 s. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Method** & **P@10(\%)** & **R@10(\%)** & **F@10(\%)** & **MAP@10(\%)** & **UCOV@10(\%)** & **AUC(\%)** \\ \hline MostPop & 11.2547 & 2.4430 & 2.0073 & 7.1047 & 44.1653 & 72.0688 \\ SVD++ & 0.0051 & 0.0004 & 0.0004 & 0.0014 & 0.0509 \(\pm\) 0.01 & 50.0254 \(\pm\) 0.01 \\ AoBPR & \(7.9151\pm 0.41\) & \(1.4077\pm 0.09\) & \(1.1950\pm 0.07\) & \(4.0941\pm 0.30\) & \(37.3732\pm 1.33\) & 68.6738 \(\pm\) 0.66 \\ WRMF & 10.6451 & 2.0716 & 1.7341 & 6.2792 & 42.1231 \(\pm\) 0.01 & 71.0481 \(\pm\) 0.01 \\ UserKNN & 13.5335 & 2.8104 & 2.3272 & 8.1964 & 52.0682 & 76.0176 \\ RBM & 0.0204 & 0.0015 & 0.0014 & 0.0066 & 0.2044 & 50.1021 \\ LSTM & \(8.7646\pm 0.77\) & \(1.8996\pm 0.14\) & \(1.5611\pm 0.12\) & \(4.9397\pm 0.56\) & \(39.7728\pm 2.06\) & 69.8735 \(\pm\) 1.03 \\ D-ULSB & \(\mathbf{14.2612\pm 0.60}\) & \(\mathbf{3.6349\pm 0.21}\) & \(\mathbf{2.8950\pm 0.14}\) & \(\mathbf{8.3833\pm 0.53}\) & \(\mathbf{58.0929\pm 1.14}\) & \(\mathbf{79.0296\pm 0.57}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparation between DTNMR and the seven baseline methods. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Method** & **P@10(\%)** & **R@10(\%)** & **F@10(\%)** & **MAP@10(\%)** & **UCOV@10(\%)** & **AUC(\%)** \\ \hline D-L & \(13.2910\pm 0.91\) & \(3.4251\pm 0.29\) & \(2.7220\pm 0.22\) & \(7.6190\pm 0.94\) & \(56.4723\pm 1.99\) & \(78.2196\pm 0.99\) \\ D-U & \(13.0346\pm 0.99\) & \(3.2040\pm 0.54\) & \(2.5675\pm 0.39\) & \(7.5343\pm 1.23\) & \(54.9940\pm 3.14\) & \(77.4807\pm 1.57\) \\ D-UL & \(13.3159\pm 1.20\) & \(3.1545\pm 0.42\) & \(2.5464\pm 0.31\) & \(7.6950\pm 0.88\) & \(54.2578\pm 2.84\) & \(77.7127\pm 1.42\) \\ D-ULS & \(13.4966\pm 0.91\) & \(3.3300\pm 0.40\) & \(2.6683\pm 0.28\) & \(8.0431\pm 1.14\) & \(55.8836\pm 2.21\) & \(77.9254\pm 1.11\) \\ D-ULSB & \(\mathbf{14.2612\pm 0.60}\) & \(\mathbf{3.6349\pm 0.21}\) & \(\mathbf{2.8950\pm 0.14}\) & \(\mathbf{8.3833\pm 0.53}\) & \(\mathbf{58.0929\pm 1.14}\) & \(\mathbf{79.0296\pm 0.57}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The influence of the four components in DTNMR (D denotes DTNMR for short, the component names after “-” mean the corresponding component is turned on, where S is user short-term preference, L is user long-term preference, U is user intrinsic preference and B is the behavior types). DTNMR recommends songs more precisely than other methods by considering music characteristics and users' temporal preferences. The music characteristics are extracted from their metadata by a deep neural network. The user preferences are extracted from the users listen histories with RNNs and DNNs. In addition, DTNMR overcomes the sparse data problem by using the metadata. Since metadata exists in all songs and users, DTNMR can describe songs and users more precisely. DTNMR outperformed UserKNN by 0.73%, 0.82%, 0.57%, 0.19%, 6.03% and 3.01% in terms of Precision, Recall, F-Measure, MAP, User Coverage and AUC. Compared with MostPop, these values are 2.71%, 1.19%, 0.89%, 1.28%, 14.07% and 6.96% respectively. Finally, we conducted an experiment to examine the ability of recommending songs to new users and to demonstrate that DTNMR can learn new preferences for users. In this experiment, 10 percents of users were removed from the training set.