API Recommendation For Mashup Creation: A Comprehensive Survey.

This article provides a comprehensive survey of web API recommendation systems designed to facilitate mashup creation, focusing on methods that assist developers in selecting appropriate APIs from the rapidly growing number available online. It reviews traditional recommendation techniques such as collaborative filtering (CF), content-based filtering (CBF), and hybrid models, as well as more recent advances involving network representation learning and deep learning approaches that aim to address challenges like data sparsity, cold-start problems, and scalability. The survey also discusses the use of knowledge graphs, attention mechanisms, and reinforcement learning in improving recommendation accuracy and diversity. Finally, it highlights emerging research directions including bootstrapping cold-start items, leveraging external side information, on-the-fly recommendations, explainable recommendation models, and user-centric tools, emphasizing ongoing opportunities to enhance API recommendation effectiveness for mashup developers.