# Information Fusion-Based Deep Neural Attentive Matrix Factorization Recommendation
###### Abstract  
The emergence of the recommendation system has effectively alleviated the information overload problem. However, traditional recommendation systems either ignore the rich attribute information of users and items, such as the user's social-demographic features, the item's content features, etc., facing the sparsity problem, or adopt the fully connected network to concatenate the attribute information, ignoring the interaction between the attribute information. In this paper, we propose the information fusion-based deep neural attentive matrix factorization (IFDNAMF) recommendation model, which introduces the attribute information and adopts the element-wise product between the different information domains to learn the cross-features when conducting information fusion. In addition, the attention mechanism is utilized to distinguish the importance of different cross-features on prediction results. In addition, the IFDNAMF adopts the deep neural network to learn the high-order interaction between users and items. Meanwhile, we conduct extensive experiments on two datasets: MovieLens and Book-crossing, and demonstrate the feasibility and effectiveness of the model. attention mechanism; cross-features; deep neural network; information fusion; matrix factorization; recommendation system +
Footnote †: journal:  
0000-0001-9991-4281-4/10/0000-0001-9991-4281-4/10/00001-9991-4281-4/10/00001-9991-4281-4/10/000001-9991-4281-4/10/00001-9991-4281-4/10/00001-9991-4281-4/10/00001-999  
features (category, introduction, etc. ), etc. By adopting information fusion technology [6], the recommendation system could integrate the auxiliary information into the modeling process of the recommendation system, so the recommendation system could model user preference and items representation more comprehensively, which could enhance the expressive ability of the model and improve the performance of recommendations, as well as increase the novelty of the recommendation results. Based on the characteristic that deep neural network can fuse arbitrary continuous features and category features, some recommendation systems try to introduce auxiliary information into matrix factorization-based recommendation system. By utilizing neural networks, the recommendation system can extract feature representations of auxiliary information and combine them with the matrix factorization. It improves the model's expression ability. However, when fusing the feature representations extracted from the auxiliary information, most of models utilize the fully connected networks to combine and transform these features, which combines different categories of feature vectors by simple splicing. We can generally understand the method that the fully connected network processes feature vectors as addition operations, and addition is equivalent to the \"or\" relationship in logic. Therefore, such a feature fusion method can be simply and intuitively understood as an \"or\" operation. However, the \"or\" operation ignores the interaction between different feature domains. In practical applications, the interaction of features, that is, the \"and\" relationship between features, often contains higher-value information. Therefore, other feature combination methods are requested to introduce the \"and\" relationship between features. In this paper, we propose the information fusion-based deep neural attentive matrix factorization (IFDNAMF) recommendation model. We introduce the auxiliary information to the model based on the feature that deep neural network can fuse arbitrary continuous features and category features. When conducting information fusion, to complete the interactive fusion between features of different information domains and to get different cross-features in a targeted way, we adopt the element-wise product. Meanwhile, to distinguish the importance of diverse cross-features on recommendation results, we also introduce the attention mechanism to learn the weights of different cross-features.