# A Deep Temporal Neural Music Recommendation Model Utilizing Music and User Metadata
## 2 Related Work  
Collaborative filtering is a popular method for recommendation systems. The basic idea of collaborative filtering is recommending items according the users with similar interests [1] or recommending items similar with the items liked by the corresponding user [2]. These methods need to calculate similarities between each pairs of users or items and costs a great deal of computing resources. Consequently, matrix-based methods are proposed to solve the computing problem. These methods include Matrix Factorization [3; 4; 5], Probabilistic Latent Semantic Analysis [9], Bayesian Networks [10] and Random Walk [11; 12]. [PERSON] and [PERSON] [13] integrate metadata into coupled poisson factorization model to handle not only sparse but also popular ratings in very large-scaled data. Content based recommendation systems utilize item contents to calculate the similarities between items and recommend similar items for users. Text categorization techniques are used to recommend movies in [PERSON] et al. [14]. FOAFing the music [15] describes users interests based on their FOAF (friends of a friend) interests and habits. The interests are used to search artists and bands that the user is interested in [PERSON] et al. [16]. utilize a linear regression framework to determine the weights of different features. [PERSON] and [PERSON] [17] evaluate the usage of metadata information in content -based recommendation systems. [PERSON] et al. [18] compute the similarities between music based on their Mellow, Unpretentious, Sophisticated, Intense and Contemporary attributes. These attributes are extracted from the auditory modulation features of music. Deep learning based methods have been successfully adapted to multiple domains including computing vision, nature language processing and speech recognition. It has also been applied to recommendation systems in recent years. The first deep learning based recommendation model is proposed by [PERSON] et al. [19]. They utilized Restricted Boltzmann Machines (RBM) to predict the rating of movies. [PERSON] et al. [6] utilized Convolutional Neural Network (CNN) to extract hidden information from music. [PERSON] et al. [20] proposed a collaborative deep learning model which combined stacked denoising autoencoders [21] and collaborative topic regression together. Consequently, [PERSON] et al. [22] proposed that the pair-wised learning model collaborative deep ranking is better than collaborative deep learning for Top-K recommendations. [PERSON] et al. [23] combined Probabilistic Matrix Factorization with Convolutional Neural Network because the rating information was extremely sparse. Auto-Encoder is also used to recommend items by [PERSON] et al. [24], [PERSON] et al. [25], [PERSON] et al. [26] and [PERSON] et al. [27]. [PERSON] et al. [28] utilized social metadata to construct music-user network and perform link prediction on the obtained network to recommend music or video for users. [PERSON] et al. [29] learned music embedding results based on users' historical listening sequences and the metadata of music. Recurrent Neural Network (RNN) is also used to recommend items. [PERSON] et al. [30] utilized RNN to predict users' future behaviors in session-based recommendation scenarios. [PERSON] et al. [31] and [PERSON] and [PERSON] [32] modeled users' dynamic preferences with RNNs. [PERSON] and [PERSON] [33] proposed that RNN is also useful in normal collaborative filtering datasets. [PERSON] et al. [34] combined deep neural networks and RNNs to recommend articles for users. [PERSON] et al. [8] learned users' preferences with RNN and recommended music based on the distance between user preference and music. As collaborative filtering methods rely on the user rating information, the items' and users' characteristics are neglected. The cold start problem is another weak point of CF methods. Content-based recommendation systems calculate item similarities based on their contents or descriptions.