With increasing presence and adoption of Web Services on the World Wide Web, to recommend suitable services to users has become an important issue. However, existing personalization approaches, such as collaborative filtering or content based recommendations, are ignoring services' sociability because of the isolation of services without social relationships among them, and lacking of consideration of social influence. Therefore, there is a need for more accurate means to interlink them in a social-enhanced interest network, and to analyze and quantify the social influence. In this paper, we propose a methodology to connect distributed services into a global social service network for social influence-aware service recommendation, called recommend-as-you-go. First, we propose a novel platform to construct a global social service network by linking distributed services with social link using quality of social link, and then we propose a flexible model for effective awareness of social influence to provide a quantitative measure of the influential strength, Next, a novel social influence-aware service recommendation approach is presented based on global social service network, and finally, the experiment results show that our new approach can solve the quality of service recommendation problem well with quick query response, low usage threshold and high accuracy with user preferences by recommend-as-you-go.