# Information Fusion-Based Deep Neural Attentive Matrix Factorization Recommendation
## 2 Related Work
However, when fusing the features of various information, most of these researches utilize the traditional fully connected network to simply concatenate the features to obtain the implicit feature representations of users and items. In brief,we can understand this combination mode as \"or\" in logic. This approach has certain limitations and it is not sufficient to model the interaction between features. In other words, the fully connected network cannot obtain cross-features, and cannot express the \"and\" relationship between different features, but in practical applications, the \"and\" relationship between features often contains high-value information, efficiently utilizing this information could assist the model in further improving the performance of recommendation systems. Similarly, [PERSON] et al. [22] propose a recommendation framework, namely DVMF, which introduces the implicit feedback information and side information to model low-dimensional feature representation of users and items and greatly improves the recommended performance. However, DVMF still combines the features of side information by splicing, which limits the model performance. Based on the MF and the LSTM, [PERSON] et al. [23] proposed a novel probability framework, named as joint matrix factorization (JMF), which can effectively extract side information to form latent vectors. It also concates the features of the side information by splicing and ignores the interaction of the feature of side information. In addition, [PERSON] et al. [24] propose a hybrid recommendation model based on user ratings, reviews and social data. Social relationship and reviews information are used as auxiliary information to improve recommendation performance. [PERSON] et al. [25] propose a context-based recommendation model to improve item feature extraction, which extracts context features through convolutional neural network (CNN), it not only resolve the sparsity problem, but also addresses the information loss due to the negative values in latent factors. Although both of them introduce auxiliary information to improve recommendation performance, they ignore the interaction between different features. Of course, some works think about the interaction between the features of side information. For instance, in fusing auxiliary information, [PERSON] et al. [26] take into account capturing the correlation information between different types of side information, they put forward the HAF framework, which using heterogeneous network to unified model various side information and capture the interaction relationship between different types of side information. To some extent, the performance of recommendation system gets improved. However, when combining features, they still adopt simple splicing, so the side information still cannot be fully utilized to improve performance. In addition, the method generating metagraphs by hand-crafting requires a lot of labor costs and recommendation performance is easily affected by experience. Therefore, the IFDNAMF proposed in this paper introduces a large amount of auxiliary information into the matrix factorization-based recommendation model. When conducting the auxiliary information fusion, a targeted neural network structure is adopted to complete the cross fusion of information of different feature domains in a targeted manner, by which the recommendation system could model user preferences and item representations more comprehensively and accurately. In addition, the performance of traditional recommendation systems based on matrix factorization is limited by simple linear inner product, although some recommendation tasks [27; 28; 29; 30] have been done to improve the matrix factorization model of recommendation system in different directions, which improves the model's performance of recommendation systems, the performance of recommendation model is still limited by employing the inner product to model the interaction between users and items.