With the emergence of Web 2.0 and microservices architecture, the number of Web APIs has increased dramatically, further intensifying the demand for efficient Web API recommendation. Existing solutions typically fall into two categories: recommendation-type methods, which treat each API as a label for classification, and match-type methods, which focus on matching mashups through API retrieval. However, three critical challenges persist: 1) the semantic ambiguities in comparing API and mashup descriptions, 2) the lack of detailed comparisons between the individual API and the mashup in recommendation-type methods, and 3) time inefficiencies for API retrieval in match-type methods. To address these challenges, we propose WARBERT, a hierarchical BERT-based model for Web API recommendation. WARBERT leverages dual-component feature fusion and attention comparison to extract precise semantic representations of API and mashup descriptions. WARBERT consists of two main components: WARBERT(R) for Recommendation and WARBERT(M) for Matching. Specifically, WAR-BERT(R) serves as an initial filter, narrowing down the candidate APIs, while WARBERT(M) refines the matching process by calculating the similarity between candidate APIs and mashup. The final likelihood of a mashup being matched with an API is determined by combining the predictions from WARBERT(R) and WARBERT(M). Additionally, WARBERT(R) incorporates an auxiliary task of mashup category judgment, which enhances its effectiveness in candidate selection. Experimental results on the ProgrammableWeb dataset demonstrate that WARBERT outperforms most existing solutions and achieves improvements of up to 11.7% compared to the model MTFM (Multi-Task Fusion Model), delivering significant enhancements in accuracy and effiency.
Xu et al. (Sat,) studied this question.