# Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems
## 2 Related Work  
Collaborative filtering (CF) is widely used in service recommendation. [PERSON] et al. [15] standardized the QoS value and proposed a user-specific CF algorithm. [PERSON] et al. [16] proposed a hybrid model, which integrated service-based CF and latent semantic analysis. [PERSON] et al. [17] also proposed a CF-based hybrid model, which was a linear combination of user-based CF and service-based CF. The authors also extended the traditional PCC (Pearson Correlation Coefficient). Some researchers have found that the context information, such as network location and geographical location, is closely related to the QoS of a service [18]. Some researchers integrated such context information into the CF algorithm, achieving better prediction accuracy. [PERSON] et al. [19] used the bottom-up hierarchical clustering algorithm with utilizing the user geographical location to mine the similar region, and further integrated the region information into the CF algorithm. [PERSON] et al. [20] proposed a location-aware CF algorithm, which identified each potential autonomous system (AS) based on the IP addresses of users and services. The proposed algorithm partitioned the users and services into different groups according to the corresponding autonomous system, to select similar neighbors. The authors also extended the PCC and the CF algorithm, to reflect the similarity relationship in the same autonomous system. [PERSON] et al. [21] proposed an extended latent semantic analysis model utilizing network location for service recommendation. The latent semantic analysis model is also widely used in recommender systems, and has high flexibility and diversity. In detail, the authors modeled the network location information to the regularization term, and factorized the proposed model as SVD (Singular Value Decomposition). [PERSON] et al. [22] proposed a content-based CF algorithm, which utilized the description content extracted from the WSDL (Web Services Description Language) files, to mine the user preference for service invocation. [PERSON] et al. [23] designed a location-based hierarchical matrix factorization (HMF) method for service recommendation to acquire high accuracy QoS prediction. In addition, the time factor plays a very important role in QoS prediction. [PERSON] et al. [24] proposed a time-aware CF for QoS-based service recommendation algorithm to calculate the missing QoS values. In order to handle cold start problem in service recommendation, [PERSON] et al. proposed a location-based matrix factorization technique via preference propagation (LMF-PP) [25]. LMF-PP used the local information and preference propagation to get exact QoS prediction. [PERSON] et al. [26] proposed a novel matrix factorization approach which integrated both network and service neighborhood information into matrix factorization. [PERSON] et al. proposed a QoS prediction approach for multi-dimensional QoS data which considered all the dimensions of QoS data and performed the prediction [27]. The authors in [28] proposed a credibility-aware QoS prediction method (named CAP) to solve the problem. CAP used two-phase K-means clustering to identify the untrustworthy QoS values, then predicted the missing QoS values by the credible clustering information. [PERSON] et al. proposed a CF model, which was based on the PGraph to represent neighborhood [29]. Note that, under the case of high data sparsity, CF and its extended algorithms are likely to achieve quite low prediction accuracy. In recent years, some researchers also try to solve the recommendation deficiency issue in high sparsity case. [PERSON] et al. [30] extended the CF algorithm by designing a sparse matrix completion method. The proposed algorithm first observed the missing value, and filled a value according to the predefined rules in an iterative manner. [PERSON] and [PERSON] [31] proposed a random walk-based movie recommendation model.