# A Deep Temporal Neural Music Recommendation Model Utilizing Music and User Metadata
## 4 Experiments
### Experiment Results
After training the DTNMR with the remaining data, we utilized the trained model to recommend songs for the removed users. Each user's recommendation results were based on their listening histories in the original training set, and the evaluation result was based on the test set. The performance of recommending songs for these users of DTNMR was 13.6077 \(\pm\) 0.82, 3.2026 \(\pm\) 0.41, 2.5897 \(\pm\) 0.29, 7.9849 \(\pm\) 0.72 and 55.7587 \(\pm\) 2.24 in terms of P@10, R@10, F-M@10, MAP@10, UCOV@10 respectively. This performance was similar with training DTNMR with the whole training set. When DTNMR did not know any information about the users, it infered the behavior patterns from existing users and recommend new users accordingly. The long-term and short-term components described users dynamic preferences based on their recently actions. Therefore, users' dynamic preferences changed when they listen to new songs. Initially, there was no dynamic preferences for the new users, and after some actions, DTNMR described their dynamic preferences based on the new records. This characteristic was guaranteed by the LSTM mechanism. LSTM is developed for learning sequential information from sequence input. It's performance is verified in multiple domains such as language modeling, machine translation, speech recognition and image descriptions generation. In DTNMR, LSTM is used to learn users' preference from their recently actions, and the preference varied while the actions changed. The intrinsic preferences varied much slower than dynamic preferences since it recorded the users' entire histories. Table 7 shows the recommendation results of two users at different times. In this table, bold songs are the correct recommendation. From this table, we found that the recommendation results varied as time changed. For the user 1500, song 212,724 ranked higher at time \(t1\) than that at time \(t2\). A similar phenomenon existed in the results of user 13,207, where song 190,396 ranked higher at time \(t3\). The users' intrinsic preferences remain the same as time changed, therefore, this phenomenon was caused by the different user dynamic preferences at different times. In other words, DTNMR predicted that the user 1500 was more interested in song 212,724 when \(t1\) than \(t2\) since the dynamic preferences generated by LSTM changed. As a conclusion, DTNMR could automatically adapt to new users and learns new features when they listened to more songs, and LSTM could learn different dynamic preferences of the same user at different times. To recommend songs to users without listening histories, DTNMR asked the users to select their interested genres or artists, and recommended relevant and popular songs based on their selection. \begin{table}
\begin{tabular}{l l} \hline User 1500 & Recommendation Result \\ Time \(t1\) & **246685** 170780 **366048** 415259 416067 419466 **212724** 152266 17662 \\ Time \(t2\) & **246685** 170780 **366048** 415259 416067 419466 152266 **212724** 17662 \\ \hline User 13207 & Recommendation Result \\ Time \(t3\) & 366048 79938 416067 419466 119903 354948 **190396** 26854 308565 \\ Time \(t4\) & 366048 79938 416067 419466 119903 354948 26854 **190396** 308565 \\ \hline \end{tabular}
\end{table} 
Table 7: The recommendation results of user 1500 and user 13207 at different time.