As service-oriented architecture becoming one prevalent technique to rapidly compose functionalities to customers, increasingly more reusable software components have been published online in the form of web services. To create a mashup, however, it gets not only time-consuming but also error-prone for developers to find suitable services components from such a sea of services. Service discovery and recommendation has thus attracted significant momentum in both academia and industry. This paper proposes a novel incremental recommend-as-you-go approach to recommending next potential service based on the context of a mashup under construction, considering services that have been selected up to the current step as well as the mashup goal. The core technique is an algorithm of learning the embedding of services, which learns their past goal-driven context-aware decision making behaviors in addition to their semantic descriptions and co-occurrence history. A goal exclusionary negative sampling mechanism tailored for mashup development is also developed to improve training performance. Extensive experiments on a real-world dataset demonstrate the effectiveness of this approach.