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

arXiv: https://arxiv.org/abs/2509.23175 · HTML: https://mlsanity.com/paper/2509.23175/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 52 references that are in the map.
- First posted on arXiv 27 Sep 2025.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://mlsanity.com/paper/1908.08962/) (2019)
- [Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics](https://mlsanity.com/paper/2110.01518/) (2021)
- [Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer](https://mlsanity.com/paper/2310.12442/) (2023)
- [How to make latent factors interpretable by feeding Factorization machines with knowledge graphs](https://mlsanity.com/paper/1909.05038/) (2019)
- [Distributed Redundant Placement for Microservice-based Applications at the Edge](https://mlsanity.com/paper/1911.03600/) (2019)
- [Deep Learning Framework for Multi-Round Service Bundle Recommendation in Iterative Mashup Development](https://mlsanity.com/paper/2101.02836/) (2021)

## Abstract (arXiv)

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.