# Location-Aware Deep Interaction Forest for Web Service QoS Prediction
## 1 Introduction  
In recent years, most computer software has transformed from native software to web-based software [1]. With the widespread adoption of technologies such as service-oriented architecture (SOA), the Internet of Services (IoS), and cloud computing, an increasing number of enterprises are launching various virtual services to meet user demands. Data, artificial intelligence, and mobile applications also continue to provide new technological support for enterprises to maintain a competitive advantage [2; 3; 4]. Nowadays, in the face of the increasing number of services with similar functions, it is difficult for users to make appropriate choices. How to evaluate and screen high-quality services from the massive number of services has become a tricky issue. In this case, investigating and comparing quality of service (QoS) has become a vital reference for service recommendation. QoS is a key indicator to describe and evaluate web services' quality [5; 6]. Many studies focus on the prediction of QoS to achieve service recommendation, such as response time (RT), throughput (TP), and so on. There are many methods for predicting QoS today, most of which are inspired by collaborative filtering for service recommendation [7; 8], which completes the prediction of missing QoS by mining historical similarity information of users or services. These methods mainly use information from the original user-service QoSmatrix, which is easily affected by data sparsity and ignores some important influencing factors, such as location information [9]. There are certain limitations. In a real scenario, the QoS value recorded by calling the real service number only accounts for a very small part of the QoS value of all web services, and the extremely sparse data bring difficulties to QoS prediction. With the continuous upgrading and development of deep learning and computing environments, many advanced deep learning methods have been produced, such as those based on deep neural networks (DNN), which have outstanding performance in effectively solving complex problems. Therefore, with the help of deep learning models, which have the ability to automatically learn feature interactive information [10], many scholars solve the problem of feature interaction information fusion and improve the accuracy of QoS prediction. Traditional research relies on the artificial extraction of low-order feature interaction information, and deep neural networks tend to learn implicit high-order feature interactions, resulting in unnecessary computations. Although existing models have achieved great improvements in prediction accuracy, most models do not structurally enhance the fusion of feature interaction information, and cannot model feature interactions efficiently and explicitly. As an exploratory deep model of non-neural network structures, deep forests [11] have certain advantages in multigranularity scanning to process feature relationships and characterize high-order feature interactions. Therefore, in response to the above problems, this paper proposes a location-aware deep interactive forest method based on the idea of deep forests. The main contributions of this article are as follows:  
* We designed a scanning interactive structure (SIS). It interacts user features and service features, and selects different size combinations of local features to generate more effective feature interaction information and alleviate the effect of sparse data. It also designs a new interactive calculation to express the feature interaction information under different orders. * We proposed the LDIF model based on deep forests. By fusing the location similarity information of users and services, and using SIS to compose a layer-by-layer cascade, which automatically fuses feature interaction information from low- to high-order, it can find more effective feature information and improve the accuracy of QoS prediction.