Web API is an efficient and cost-effective method for service-oriented software development, and Mashup is a popular technology which combines multiple services to create more powerful services to address the increasing complexity of business requirements and speed up the software development process. Here, accurate and efficient API recommendation is vital for successful Mashup development. Currently, many existing methods combine various technologies and adopt diverse features, which results in complex models at the cost of higher computational overhead but with very limited improvement on recommendation accuracy. To address such an issue, in this paper, we propose an unsupervised API recommendation method based on deep random walks on knowledge graph. Specifically, we first construct a refined knowledge graph utilizing Mashup-API co-invocation patterns and service category attributes, and then we learn implicit low-dimensional embedding representations of entities from truncated random walks by treating walks as the equivalent of sentences. Meanwhile, to improve the recommendation accuracy, we design an entity bias procedure to reflect different entity preference (namely API-based neighborhood or Mashup-based neighborhood). Finally, we estimate the relevance between Mashup requirements and the existing services (Mashups and APIs) to obtain the API recommendation list. Since the API recommendation results can be obtained through unsupervised feature learning, automatic API recommendation can be provided for Mashup developers in real time. Comprehensive experimental results on a real-world dataset demonstrate that our proposed method can outperform several state-of-the-art methods in both recommendation accuracy and efficiency.

Web API是用于面向服务的软件开发的一种有效且具有成本效益的方法，而Mashup是一种流行的技术，它结合了多种服务以创建功能更强大的服务，以解决业务需求日益复杂的问题并加快软件开发过程。在这里，准确有效的API建议对于成功进行Mashup开发至关重要。当前，许多现有方法结合了各种技术并采用了多种功能，这导致了复杂的模型，但付出了更高的计算开销，但对推荐准确性的改进却非常有限。为了解决这个问题，本文提出了一种基于知识图上深度随机游走的无监督API推荐方法。具体来说，我们首先利用Mashup-API联合调用模式和服务类别属性构造了一个精炼的知识图，然后我们通过将游走视为句子的等效项来从截断的随机游走中学习实体的隐式低维嵌入表示。同时，为了提高推荐的准确性，我们设计了一种实体偏向过程来反映不同的实体偏好（即基于API的邻域或基于Mashup的邻域）。最后，我们估计Mashup要求与现有服务（Mashups和API）之间的相关性，以获得API推荐列表。由于可以通过无监督的特征学习获得API推荐结果，因此可以为Mashup开发人员实时提供自动API推荐。