Manifold-Learning Based API Recommendation for Mashup Creation

With the wide adoption of Service-Oriented Architecture (SOA), the number of web accessible services and their compositions is increasing rapidly. Among huge number of services, how to recommend appropriate ones for automatic composition satisfying users' need is challenging. We investigate services and their compositions in Programmable Web which characterize services as APIs and their compositions as mashups. We study the problem of recommending suitable APIs satisfying users' need for mash up creation. To this end, we propose a manifold ranking framework for API recommendation. First, we categorize existing mashups into functionally similar clusters. Then we recommend APIs for each mash up cluster using manifold ranking algorithm which incorporate the relationships between mashups, between APIs and between mashups and APIs. Intuitively, we take three factors into consideration: (1) We recommend APIs that are in functionally similar mashups. (2) We recommend APIs that are popular in the mashups. (3) We recommend APIs that are similar to each other. Finally, we map a user's requirement for mash up creation to a mash up cluster and recommend APIs generated by the algorithm to user. Experiments based on real dataset crawled from Programmble Web demonstrate the effectiveness of the proposed approach in terms of precision, recall, and NDCG.