# Information Fusion-Based Deep Neural Attentive Matrix Factorization Recommendation
## 3 Preliminary Work
### Feature Crosses-Based Information Fusion  
Traditional recommendation models often take rating information composed of explicit feedback data and interaction information composed of implicit feedback data as input, which leads to the model can only model partial user preferences and partial item representations because of the high sparsity of the data. Therefore, we introduce auxiliary information, namely the user's social-demographic features (age, gender, occupation, etc.) and the item's content features (categories, profiles, etc.) into the IFDNAMF model. Specifically, we utilize one-hot encoding to obtain the representations of category features (age, gender, ID, etc.). To solve the problem that the number of categorical features is large, which results in very sparse binary vectors, we adopt the embedding matrix to map the sparse and high-dimensional vector representations to the dense and low-dimensional space. Meanwhile, we adopt Fast-Text [34] to obtain the text attribute representations by averaging word embedding vectors. Similarly, we multiply the text attribute representations by the embedding matrix to map the sparse vector representations to the dense space. As introduced in Section 2, when conducting the auxiliary information fusion, most of the existing researches utilize the fully connected network to simply concatenate the features of different information, which can be regarded as the \"or\" operation in logic. The fully connected network cannot obtain cross-features and express the interaction between features, which makes the model ignore the \"and\" relationship between features. Considering a scenario in which ad click predictions are made for different users on the sites to recommend ads that meet the personalized needs of users. For the Disney advertisement on the sites, we assume that these features, namely the location: Shanghai, younger users, and the date: Friday, play a critical role in whether the user clicks it. Users who meet these features are more likely to click on this ad when they see this ad. Intuitively, users matching the above features have a higher probability of clicking than users who only match one of them. In other words, the value of the information contained in the \"and\" relationship between the features is richer than the value of the information contained in the \"or\" relationship  
Figure 1: Information fusion-based deep neural attentive matrix factorization recommendation model structure. between the features. Ignoring this \"and\" relationship between features may result in the loss of much useful information in the modeling process, which may affect the performance of recommendation systems of the model. Therefore, to introduce the information contained in the \"and\" relationship between features, we propose an information fusion method based on feature crosses in the IFD-NAMF recommendation model. The specific implementation is shown in the interaction layer in Figure 1. When fusing the auxiliary information, we adopt the element-wise product between different features to complete the interaction between features of different information domains in a targeted way and obtain different cross-features. That is, we perform the element-wise product between the user ID and each user attribute feature and the pairwise element-wise product between the user attribute features for the user, and we adopt the element-wise product to model the interaction between the item ID and each item attribute feature and the interaction between the item attribute features for the item. The pairwise element-wise product in Figure 1 is the operation of the element-wise product between the embedding vectors described above.