Leveraging Co-Occurrence Bias in Web API Recommendation via Causality-Inspired Context-Adjusted Graph Learning

Abstract

Web API recommendation is crucial for mashup development, yet the rapid growth of service repositories causes severe information overload. Existing methods heavily exploit historical API co-occurrences, which can introduce spurious correlations bias across heterogeneous contexts and aggravate sparsity by ignoring valid but unobserved combinations. We propose CICA, a causality-inspired, context-adjusted graph learning framework that (i) disentangles functional semantics from contextual confounders and employs a Query-Aware Graph Neural Network to selectively activate context-relevant edges for dynamic noise filtering. Furthermore, the framework (ii) enriches sparse nodes with implicit semantic similarity edges to alleviate the sparsity bias. Finally, a causal adjustment mechanism is implemented to integrate functional matching with co-occurrence patterns by dynamically controlling the intervention of co-occurrence bias. Comprehensive experiments conducted on the real-world ProgrammableWeb dataset demonstrate that CICA consistently outperforms state-of-the-art baselines across standard evaluation metrics, including Precision, Recall, and NDCG.