Quality-aware Web API recommendation for Mashup

The number of Web APIs on the Internet has increased in recent years. How to recommend a suitable high-quality Web API for Mashup applications has become a hot research topic. Ignoring the influence of Web API quality information on recommendations has restricted the performance of the existing technical methods. Deep learning technology provides a new solution to further improve the accuracy of Web API recommendations. How to use Web API quality information combined with deep network models for high-precision recommendations has also become a key issue. To this end, this paper proposes a Web API quality-aware deep recommendation model, which combines deep neural networks and quality information modelling to improve the accuracy of Web API recommendations. First, we use the BERT pre-training model as a text encoder to extract the text description features of Mashup and Web API, then utilize the self-attention mechanism to fuse Web API quality information, and finally make recommendations based on the obtained Web API quality enhancement features. Compared with the baseline model of Web API tasks based on real dataset, the experimental results prove that our method improves the top-1 accuracy, recall, and NDCG indicators by 3.97%, 3.45% and 3.97% respectively