用分层BERT模型提升网页API推荐准确率与效率
WARBERT: A Hierarchical BERT-based Model for Web API Recommendation
- 分两阶段建模：先分类筛选，再精细匹配
- 在ProgrammableWeb数据集上准确率显著超越基线
- 适合需要高效精准API推荐的开发者和平台
随着Web 2.0和微服务兴起，网页API数量激增，亟需高效的推荐系统。现有方法分为分类型（基于标签）和匹配型（通过混搭程序检索），但仍面临三大挑战：1）API与混搭描述间语义歧义；2）混搭需求与单个API描述间缺乏渐进式语义细化；3）大规模仓库中全量混搭-API比较计算低效。为此，我们提出WARBERT，一种基于BERT的分层模型。该模型采用双组件特征融合与注意力机制，构建精准语义表示。包含WARBERT(R)用于初始候选过滤（基于分类），及WARBERT(M)用于精细化相似度匹配。最终配对概率融合两组件预测结果，其中WARBERT(R)通过辅助任务预测混搭类别进一步优化。在ProgrammableWeb数据集上的实验表明，WARBERT显著优于现有基线，在准确率与效率方面均有明显提升。
原文摘要 · Abstract (English)
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.
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