Exploiting Heterogeneous Information for Tag Recommendation in API Management

As web-enabled software becomes the standard for business processes, the ways organizations, partners and customers interface with it have become a critical differentiator in the market place, i.e., API Economy. With the rapid proliferation of APIs, it is increasingly important for users to effectively manage objective APIs in kinds of API markets, e.g., ProgramableWeb (PW), Mashape, etc. In this paper, to facilitate the process of API management, we propose a graphbased recommendation approach called ATRec to automatically assign tags to unlabeled APIs by exploiting both graph structure information and semantic similarity. Specifically, ATRec first leverages the multi-type relations (i.e., among APIs, mashups, and mashup assigned tags) to construct a heterogeneous network, in which a Random Walk with Restart (RWR) model is applied to alleviate the total cold start problem where no API has ever been tagged. Furthermore, we apply the recommended API tags in two API management scenarios (API search, API recommendation). Comprehensive experiments based on a real dataset crawled from PW demonstrate the effectiveness of the proposed approach.