# Information Fusion-Based Deep Neural Attentive Matrix Factorization Recommendation
## 2 Related Work  
The key of the recommendation system is the recommendation algorithm. There are mainly three recommendation algorithms: content based [8], collaborative filtering [9], and hybrid based [10], among which collaborative filtering is the most commonly used. The matrix factorization (MF) algorithm proposed by [PERSON] (Available online: [[http://sifter.org/simon/journal/20061211.html](http://sifter.org/simon/journal/20061211.html)]([http://sifter.org/simon/journal/20061211.html](http://sifter.org/simon/journal/20061211.html)) (accessed on 26 September 2021)) is an effective collaborative recommendation algorithm, which gets widely used in many real-world scenarios [11]. The traditional recommendation system based on matrix factorization decomposes the user rating matrix into the user-factor matrix and the item-factor matrix through a set of potential feature factors and utilizes potential factor vectors to describe users and items, that is, users and items are mapped into a shared latent vector space, and the interaction between users and items is expressed by the inner product between the mapped vectors, and then a Top_N recommendation list is generated for the user according to the inner product result [12]. However, the traditional matrix factorization-based recommendation system often takes the rating matrix or interaction matrix as the input. The single-form input data contain a small amount of information, thus it is difficult to model user preference and item representation comprehensively and accurately. Furthermore, MF faces the problem of high sparsity of rating matrices and interaction matrices since each user only interacts with a tiny fraction of a great quantity of items, which limits the performance of recommendation systems [13]. In fact, in practical application scenarios, users and items have a wealth of auxiliary information, such as social trust relationships [14; 15], tags [16], context [17], and so on. Introducing the auxiliary information to the model can assist the recommendation system in modeling user preferences and item representations more comprehensively, and further improve the model's performance of recommendation systems. Therefore, to make full use of the rich auxiliary information to assist in modeling user preferences and item representations, [PERSON] et al. [18] employed the auxiliary information in the recommendation model to solve users' brand recommendation task. A large number of user and item features are modeled in the model, including the user's age, acceptable price range, item materials, design methods, sewing methods, etc. These learned feature representations are mapped to the softmax output layer through multi-layer neurons with ReLU as activation function to obtain predicted ratings. Similarly, [PERSON] et al. [19] proposed a deep learning based collaborative filtering framework, namely, deep matrix factorization (DMF), which integrates various side information to generate latent factors of users and items by concatenation operation. [PERSON] et al. [20] proposed a recommendation algorithm based on neural networks, in which multiple fully connected layers are utilized to integrate rich continuous features and category features, including historical user activity records and social-demographic features (age, gender, occupation, etc. ), to improve performance of recommendation systems of the model. Furthermore, [PERSON] et al. [21] employed the neural network language model to fuse information from multiple sources, including user data, item data, and tag data, to extract rich feature representations of users and items. The above researches introduce a great quantity of auxiliary information with various forms to the model through the deep neural network, including continuous features and category features, to model user preferences and item representations more comprehensively and accurately.