  As service-oriented architecture becoming one of the most prevalent
techniques to rapidly deliver functionalities to customers, increasingly more
reusable software components have been published online in forms of web
services. To create a mashup, it gets not only time-consuming but also
error-prone for developers to find suitable services 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 to the current step as well as its 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 our approach.
