## 2 Related Work
### Deep Learning-Based Recommendation Systems  
With the development of artificial intelligence, much research showed that deep learning-based methods got a good performance in the recommendation systems. Autoencoders have been applied in CF recommenders in the last few years. A deep learning model stacked denoising autoencoder and used it in integration with probabilistic matrix factorization was adopted by [21]. To satisfy the need for relational deep learning, they proposed a probabilistic formulation for stacked denoising autoencoder and then extended it to a relational stacked denoising autoencoder model. An autoencoder framework called AutoRec for collaborative filtering was proposed by [22]. A collaborative denoising auto-encoder (CDAE) method for Top-N recommendation was presented by [23]. A deep autoencoder model trained end-to-end without any layer-wise pre-training for the rating prediction task was proposed by [24]. However, different from these works in which autoencoders are integrated into the rating prediction process, the autoencoder in our study is to extract compact content features from the high-dimensional vectors. The convolutional neural networks (CNN) to perform the hashtag recommendation problem was proposed by [25]. They proposed a novel attention-based CNN architecture to incorporate a trigger word mechanism, including a local attention channel and a global channel. Experimental results showed that the proposed method could achieve significantly better performance than the state-of-the-art methods. A dual-net deep network model to make recommendations of images to users was designed by [26]. The network consists of two sub-networks, which map an image and user preferences into the same latent semantic space. To alleviate the platform editors' working load by automating the manual article selection process and recommending a subset of articles that fits the human editor's taste and interest was aimed by [27]. They proposed a dynamic attention deep model to address the problems for the editor article recommendation task. The model used character-level text modeling and convolutional neural networks to learn the representation of each article effectively. A neural architecture called PACE to bridge collaborative filtering and semi-supervised learning for point of interest (POI) recommendation was developed by [28]. The PACE is a deep neural architecture that jointly learns the embedding of users and POIs to predict user preference over POIs and various contexts associated with users and POIs. A hybrid method called location-aware personalized news recommendation with explicit semantic analysis (LP-ESA), which recommended news using both the user's interests and geographical contexts was proposed by [29]. They further proposed a novel method called LP-DSA to exploit recommendation-oriented deep neural networks to extract dense. Experimental results showed that LP-DSA further improves the news recommendation. The inner product with neural architecture was replaced by [10]. They presented neural network architecture to model users' latent features and items to tackle the implicit collaborative filtering problem. A hybrid neural recommendation model to learn the deep representations for users and items from both ratings and reviews was proposed by [30]. Three major components: a rating-based encoder, a review-based encoder, and the prediction module, are proposed. Besides, the research introduces a novel review-level attention mechanism incorporating rating-based representation as a query vector to select valuable reviews. A novel multi-criteria collaborative filtering model based on deep learning was proposed by [31]. The model obtains the users' and items' features and uses them as an input to the criteria ratings deep neural network, which predicts the criteria ratings. Those criteria ratings then input to the overall rating deep neural network for rating prediction.