# Collaborative Web Service Discovery and Recommendation Based on Social Link
## 2 Related Work  
From recent research, leveraging latent user social information in the process of Web service discovery to improve the performance of discovery result is a feasible and effective method. In [3], the concept of community and the metaphor of social networking were discussed to build a framework to managing Web services, in which a community was proposed to gather Web services that offer similar functionalities together, and a social networking was designed to capture all interactions that occur between services located in the same or separate communities. [PERSON] et al. discussed the intertwining of social networks of users and social networks in [4]. They used the former to help users select the necessary services, and adopted the latter to permit extending these services with new services or maintaining the operation continuity in case of failure. To maximize the utilization of limited network resources, a hybrid service-demand discovery architecture for mobile social networks was introduced in [5], in which users can discover their relationships with others based on the historical data of movement. In order to address the problems of the isolation of services and the lack of social relationships in Web service discovery, [PERSON] et al. presented a global social service network connecting the isolated service islands to enhance the services' sociability on a global scale in [6]. The network can be constructed following linked social service-specific principles based on complex network theories. In [7], [PERSON] et al. proposed a web service discovery process based on users' queries and their social profiles that can be extracted from their egocentric network. The process employs the best social friendships of the current user and the past invocation histories with satisfactory web services of his friends to generate satisfactory results. To limit the disadvantages of distributed approaches, a novel distributed reputation model was presented in [8], which treats multi-agent systems as social networks and leverages social relations among these agents. Some typical social factors such as trust and clustering are also widely discussed in service discovery. A novel evaluation model for Web Service discovery by leveraging trust as an approach was introduced in [9]. The authors incorporated a trust management module into the standard Service Oriented Architecture and transformed a Web Service network to a small-world network based on the trust relationships of service entities. Then they proposed a trust evaluation model with an amendatory subjective logic. In [10], a trusted service discovery method based on trust and recommendation relationships was presented. In this method, two types of trust relationships and an open service network were both defined, and a dynamic service discovery algorithm was also proposed. Based on the intuition that a user's web service QoS usage experiences can be predicted by both the user's own characteristics and the past usage experiences of other similar users, [PERSON] et al. proposed a neighborhood-integrated matrix factorization approach for making personalized prediction in service discovery and service selecting [11], which could systematically fuse the neighborhood-based and model based collaborative filtering approaches to achieve higher prediction accuracy. A new service discovery method based on referral network and ant-colony algorithm was proposed in [12]. The approach can make use of recommendation and ant colony algorithm to improve the success and recall rate of service discovery. Based on friend relationships and interaction behaviors, a new recommender approach was introduced in [13]. The approach adopted a trust computing method and depended on an authentic social network to obtain more authentic neighbors to improve the accuracy of recommendations.