Web API Recommendation via Leveraging Content and Network Semantics
Guosheng Kang, Bowen Liang, Jianxun Liu, Yiping Wen, Yong Xiao, Hejing Nie
Abstract
With the wide adoption of SOA (Service Oriented Architecture) in software engineering, a large number of Web services have emerged to meet the Mashup development requirements. Due to the existence of numerous Web services with similar or identical functionalities, it is challenging for users to select the appropriate Web API for Mashup creation, which makes Web service recommendation an effective approach. The performance of current FM-based service recommendation methods is limited by the sparsity of semantic features related to their functionalities. Furthermore, the network structure features of Web services are often overlooked. However, these features are of great importance and should be incorporated into the service recommendation process. Based on the above considerations, this paper proposes a service recommendation model which fuses content information and network information. Firstly, service content information and network structure information are extracted respectively. Then, these two types of information are characterized separately, and their functional semantics are extracted. Finally, the above information is fused and processed by Neural and Attention Factorization Machine to obtain the final recommendation results. Experimental results show that fusing service network representation information can effectively improve the accuracy of service recommendation results.