# Location-Aware Deep Interaction Forest for Web Service QoS Prediction
## 2 Related Work
### Deep Learning Methods  
In recent years, neural networks have also been gradually applied in QoS prediction. Due to the continuous improvement in numbers of features, current service recommendation algorithms are no longer satisfied with the low-order feature interaction information in FM. More scholars use the characteristics of deep neural networks to automatically complete the fusion of low-order and high-order feature interaction information. For example, [PERSON] et al. [25] in Google thought that deep neural networks can extend higher-order invisible feature interaction information, so they proposed the Wide and Deep model, which combined polynomial regression and deep neural networks for parallel data processing. On this basis, [PERSON] et al. [26] proposed the Deep&Cross model with a Cross network instead of polynomial regression. The Cross network can be used to explicitly characterize the influence of feature interactions, and the order of feature interaction information will vary with the number of layers, which further improves the interpretability of feature interaction information and the accuracy of result prediction. For the Cross network, [PERSON] et al. [27] thought that it is only a scaled form of the original features, so proposed a new network structure (CIN) and a new recommendation model (xDeepFM), which uses vector-level interactions in different feature domains instead of element-level feature interactions. On this foundation, service recommendation algorithms evolved to integrate geographic location features; [PERSON] et al. [28] fused geolocation information by combining collaborative filtering and a multilayer perceptron. [PERSON] et al. [29] proposed a topological neural network (TAN) by describing the topological structure of the underlying network and the complex interaction between autonomous regions, which combined the path features and end-cross features through an explicit path-modeling layer and an implicit cross-modeling. If only a single type of feature is used, the problem of gradient disappearance in deep network is serious, so [PERSON] et al. [30] considered user and service historical probability distribution features and location features to reuse the ResNet network, which alleviates the gradient disappearance problem and improves the prediction accuracy. [PERSON] et al. [31] believed that the state of unknown QoS is a hidden state, so they proposed a hidden state initialization and perception approach based on LDA. Different from the above idea, [PERSON] et al. [32] employed information entropy into the combination of FM and DNN to mine more useful features. Recently, [PERSON] et al. [33] proposed a novel deep neural network model with multistage and multiscale feature fusion. This model extracted global, individual, and local features separately and implemented feature fusion using a multistage neural network architecture. Additionally, [PERSON] et al. [34] incorporated historical call similarity, constructed neighborhood subgraphs of users and services using similarity relationships, and proposed the double subgraph network (BGCL) method based on graph contrastive learning. [PERSON] et al. [35] introduced a feature mapping and inference network method to capture the deep features of users and services and made up for the loss of feature information with feature compensation blocks. [PERSON] et al. [36] investigated a Gaussian feature distribution smoothing network to address the issues of noise and label imbalance in the dataset for QoS prediction. It is worth noting that the previous methods all incorporate geographic location features or other contextual features into the model, but lack the interaction information between geographic location similarity and other features, so our approach integrates geographic location similarity features into feature interaction information based on location awareness. [PERSON] et al. [11] proposed a new deep learning model: the deep forest.