# Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems
###### Abstract  
Cyber-physical systems (CPS) have received much attention from both academia and industry. An increasing number of functions in CPS are provided in the way of services, which gives rise to an urgent task, that is, how to recommend the suitable services in a huge number of available services in CPS. In traditional service recommendation, collaborative filtering (CF) has been studied in academia, and used in industry. However, there exist several defects that limit the application of CF-based methods in CPS. One is that under the case of high data sparsity, CF-based methods are likely to generate inaccurate prediction results. In this paper, we discover that mining the potential similarity relations among users or services in CPS is really helpful to improve the prediction accuracy. Besides, most of traditional CF-based methods are only capable of using the service invocation records, but ignore the context information, such as network location, which is a typical context in CPS. In this paper, we propose a novel service recommendation method for CPS, which utilizes network location as context information and contains three prediction models using random walking. We conduct sufficient experiments on two real-world datasets, and the results demonstrate the effectiveness of our proposed methods and verify that the network location is indeed useful in QoS prediction. c 2017 12 2  
Service-oriented computing (SOC), as a popular distributed computing paradigm, is widely studied by a series of institutions and standardization organizations. Traditionally, the SOC community focuses on service composition [5], service discovery [6], service selection [7; 8; 9], and service testing [10]. The existing researches are indeed verified to be valuable in many fields, such as distributed computing, cloud computing and cyber-physical system. However, along with the explosion of resources in CPS, it becomes increasingly difficult for a user to find the suitable services. In the case of a huge data volume, it is urgent to develop an effective service recommender system. In this paper, we focus on designing an applicable service recommendation method for CPS. There are two types of properties for a service, functional properties and non-functional properties. The non-functional properties are represented by quality-of-service (QoS), such as response time, throughput and reliability [11]. In recent years, study on QoS is an important topic in service computing and distributed computing. Since a service usually runs in a dynamic network, the QoS value of a service is likely to change due to the instability of the network. Thus, it is important to predict QoS values for achieving effective service recommendation. In this paper, we use the historical invocation records of users and services to predict QoS values. We can build a user-service invocation matrix (a toy example is shown in Table 1), in which each entry represents the response time value. The entries with the question mark \"?\" indicate that the target QoS values are unknown. Compared to the large number of services, the number of services that are invoked by a user is rather small, which leads to the high sparsity of the user-service matrix. Collaborative filtering (CF) is a prediction algorithm that is employed in QoS prediction [12; 13; 14]. The CF algorithm utilizes the user consumption records to identify similar neighbors, and predicts the preference of the target user using the preferences of similar neighbors. In service recommendation, the CF algorithm utilizes the invocation records of the target user to identify similar neighbors, and further to predict the QoS values. The existing research has found that the traditional CF algorithms are hard to generate results with high accuracy, under the case that the invocation records are sparse, which is the normal case in CPS [1]. However, there are few works that try to solve this problem. Meanwhile, the QoS of a service is heavily impacted by the network location.