LRNNet: Complementary Cloud API Recommendation With Abductive Correction via Logical Reasoning Neural Network

Abstract

Cloud API recommendation systems play a vital role in tackling cloud API overloading issue and promoting a healthy API ecosystem. Recent studies mainly focus on user preferences, search keywords, and QoS, yielding API recommendations that satisfy single functional requirements or optimize non-functional QoS requirements in service-oriented software development. However, existing cloud API recommendation methods still face three key limitations: 1) insufficient support for practical complementary cloud API requirements in service-oriented software development; 2) inability to handle weak complementary relations and filter out substitute noise in modeling complementary relationships; and 3) lack of a post-recommendation verification and refinement mechanism to further improve recommendation results. To address these issues, we propose LRNNet, a Logical Reasoning Neural Network for complementary cloud API recommendation with abductive correction. LRNNet works in two phases: complementary recommendation and abductive correction. In the complementary recommendation phase, Beta probability embedding encodes query cloud APIs to facilitate logical reasoning. Subsequently, a logical reasoning unit, composed of projection, negation, and intersection logic operators, captures complementary relationships and mitigates substitute noise, and yields results complementary to the query. In the abductive correction phase, an abductive task is formulated to deduce potential reasons behind recommendation results. By backpropagating an abductive loss signal, the results are refined by minimizing the difference between predicted and true reasons. We conducted extensive experiments using two real-world cloud API datasets, demonstrating that LRNNet outperforms comparative SOTA baselines. Ablation studies and hyperparameter sensitivity analyses also confirm LRNNet’s effectiveness and practicality. The implementation code is publicly available at https://github.com/lhnngp/LRNNet.