# Location-Aware Deep Interaction Forest for Web Service QoS Prediction
## 3 Proposed Approach
### LDIF Approach  
For completing high-quality service recommendations, this approach draws on the ideas of the deep forest model. Firstly, the geographical location information of users and services is converted into dense numerical features through the Glove algorithm; the geographical location similarity between users and services is calculated, and the location information between the two is fused; then, using the innovation of the multigrain scanning mechanism in a deep forest, the interaction between user features and service features is automatically scanned to obtain multiple local feature combinations, aiming to generate more effective feature interaction information and alleviate the impact of sparse data; Thirdly, considering that the feature interaction information can be represented numerically, we think that the interaction between local features and connecting this information with the original features is a useful strategy; After that, the high- and low-order feature interaction information is automatically learned and fused using a layer-by-layer structure.  
Deep neural networks exhibit the advantage of hierarchical structure, so they have the ability to automatically fuse the interaction information of low- and high-order features, but there is an unchangeable difference between the tree structure and the neuron node structure. The tree structure is used to classify samples according to features rather than reprocess the original feature data by performing a nonlinear operation. So, how to design a layer-by-layer structure while using the tree structure to simultaneously alleviate the impact of sparse data and fuse feature interaction information becomes a tough problem. This paper designs a novel layer-by-layer structure approach based on the deep forest, which can alleviate the impact of sparse data in each layer and fuse feature interaction information though a layer-by-layer structure. Figure 2 is the overall structure of the LDIF approach.  
Figure 1: The overall flow of recommendation approach.  
In the LDIF approach, the 2-dimensional SIS layer is the layer with SIS and a scanning window size of 2, and the rest of the layers have the same naming rules. First, we specify the window size of \(\lfloor\textit{the dimension of feature}/8\rfloor\) to address issues such as the number of scans increasing when the scanning window is too small, resulting in size explosion; or when the size is too large, the dimension growing slowly, even in a deep layer. Additionally, we think that if the accuracy of prediction does not improve, it should stop generating the next layer. So, it will be judged by whether the average prediction accuracy in this layer is smaller than the previous layer. If not, it will stop generating the next layer. In the case of looping, this early stop mechanism effectively controls the complexity of the approach and the depth of layers and avoids unnecessary high-order feature interaction calculations. Under the worst case, we believe that we need to specify a maximum number of layers (\(D\)) to control the maximum complexity and max depth of this model.  
For the problem of fusing feature interaction information and the sparse data, we are rely on the processing of dimensional SIS layer. Through the above layer-by-layer structure, the final dimensional SIS layer will fuse from low-to high-order feature interaction information and make final QoS predictions. It will elaborate and demonstrate the structure of SIS in Section 3.2.2.