# A Deep Temporal Neural Music Recommendation Model Utilizing Music and User Metadata
## 1 Introduction  
With the development of mobile internet, enjoying online music by mobile phones is affordable for most users. Users may open their phones and listen to music anywhere, anytime. Many music recommendation systems use collaborative filtering (CF) methods [1; 2; 3]. CF methods, however, rely on explicit rating values to find similar users or items, while in music platforms, users listen to music but rarely rate the music. Although implicit feedback methods are alternative choices, these methods require careful parameter tuning [4; 5]. All the methods cannot address the cold start problem. When new items or users appear, the corresponding model must be retrained to update the items' or users' profiles.  
Recently, deep learning methods are incorporated into music recommendation systems to extract the users' temporal preferences and to solve the cold start problem. The music content based method learns latent factors of music to solve the item side cold start problem [6]. This method needs existing music representations which are commonly generated by a CF method. The performance is highly relevant to the chosen CF method. The user playlists based method represents music by pre-trainedword2 vec [7] results and learn users' preferences based on their listening histories [8]. Therefore, only the songs that have appeared in playlists are trained for recommendation. None of them address the user side or item side cold start problem, and the music characteristics are not fully exploited. The music characteristics and the users' temporal preferences are therefore not combined naturally, causing the relatively low performance of music recommendation.  
In order to solve these issues, we proposed a Deep Temporal Neural Music Recommendation model (DTNMR) based on music characteristics and users' temporal preferences. DTNMR utilizes Deep Neural Networks (DNN) to extract music characteristics and users' intrinsic preferences from their metadata. Additionally, Recurrent Neural Networks (RNN) are integrated to learn a users' long-term and short-term preferences from their listening histories. Since the music characteristics are extracted from music metadata, new and unpopular songs are recommended as same as other songs. User preferences are automatically updated when they listen to new songs, and the recommendation results varies accordingly. Therefore, DTNMR can recommend music for newly appeared users without retraining the model. As a consequence, DTNMR alleviates the cold start problem in item side using the music metadata and discovers a new users' preferences immediately after they listen to music by integrating DNNs and RNNs. Our contributions are listed as follows:  
1. We propose a Deep Temporal Neural Music Recommendation model based on music characteristics and users' temporal preferences. DTNMR alleviates the cold start problem in item side and discovers new users' preferences immediately after they listen to music.
2. Based on music metadata and user profiles, DTNMR utilizes Deep Neural Networks to extract music characteristics and users' intrinsic preferences. The music characteristics of new songs can be extracted as same as popular ones.
3. Recurrent Neural Networks are used to extract users' dynamic preferences. The user preference varies when new songs are listened by the user. As soon as users listen to music, their preferences are updated and new recommendations are provided.
4. We conduct a series of experiments and show that our model performs the best among the seven baseline methods in terms of precision, recall, f-measure, MAP, user coverage and AUC.
5. We proved that our model can recommend songs for new users after they listened to a mount of songs without retraining.  
The paper is organized as follows: The next section carries a review of the related work in the recommendation area. Section 3 elaborates on the Deep Temporal Neural Music Recommendation model. Section 4 shows the evaluation of our method. The last section presents the conclusion and the scope for future work.