# WARBERT: A Hierarchical BERT-based Model for Web API Recommendation

Source: [WARBERT: A Hierarchical BERT-based Model for Web API Recommendation](<https://www.sota2.com/research/paper/68fa7bb01e209131cea89e69>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-04-21T05:00:04.783Z

Authors: Zishuo Xu, Yuhong Gu, Dezhong Yao

Published: 2025-09-27T08:09:41.000Z

## Abstract

With the rise of Web 2.0 and microservices, the increasing availability of Web APIs has intensified the need for effective recommendation systems. Existing approaches are generally categorized into two methods: recommendation-type methods, which classify APIs using labels, and match-type methods, which retrieve APIs through matching with mashups. However, three significant challenges remain: 1) semantic ambiguities in comparing API and mashup descriptions, 2) a lack of progressive semantic refinement between mashup requirements and individual API descriptions, and 3) computational inefficiency of exhaustive mashup-API comparisons in large-scale repositories. To tackle these challenges, we propose WARBERT, a hierarchical model based on BERT for Web API recommendation. WARBERT utilizes dual-component feature fusion and attention mechanisms to create accurate semantic representations. It consists of WARBERT(R) for initial candidate filtering using recommendation methods, and WARBERT(M), which focuses on refined similarity matching. The final likelihood of an API-mashup pairing combines predictions from both components, with WARBERT(R) further enhanced by an auxiliary task of predicting mashup categories. Experiments conducted on the ProgrammableWeb dataset demonstrate WARBERT outperforms existing baselines, achieving notable improvements in both accuracy and efficiency.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Web API Recommendation on ProgrammableWeb (test)](<https://www.sota2.com/research/sota/web-api-recommendation-on-programmableweb-test.md>): Web API Recommendation; ProgrammableWeb (test); Precision: 66.16.

- [Mashup Category Judgment on ProgrammableWeb (test)](<https://www.sota2.com/research/sota/mashup-category-judgment-on-programmableweb-test.md>): Mashup Category Judgment; ProgrammableWeb (test); Recall@1: 0.3621.

- [Web API Recommendation on Web API Recommendation dataset](<https://www.sota2.com/research/sota/web-api-recommendation-on-web-api-recommendation-dataset.md>): Web API Recommendation; Web API Recommendation dataset; Inference Time: 0.5.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2509.23175>)

[PDF](<https://arxiv.org/pdf/2509.23175.pdf>)