Multi-representation web service recommendation system based on attention mechanism

Recently, mashup developers seek to integrate multiple services with complementary functionalities from a large amount of web services. With so many available web services, it is difficult for developers to choose the right one to develop new mashups. Therefore, it is critical to create and recommend appropriate web services for mashup developers based on their development needs. In the past, various deep models have been proposed to facilitate web service recommendation based on semantic matching of textual descriptions. However, existing deep approaches mainly match global semantic representations while ignoring descriptive structure and tag information. In this paper, we propose a multi-representation web service recommendation model, which simultaneously extracts global, local and tag representations of the description and tag information, respectively. Moreover, we propose a tag-driven attention mechanism to guide the process of information extraction. Experiments over a real-world dataset demonstrate that our proposed service recommendation algorithm can achieve remarkable performance.