Spatio-Temporal Context-Aware Web Service QoS Prediction via Contrastive Learning

Accurate Quality of Service (QoS) prediction is a major challenge in the field of service recommendation. Existing QoS prediction methods typically predict missing QoS values by analyzing historical call records of users and services. While these methods demonstrate effectiveness, they face limitations in addressing the sparsity of user service call data and fully utilizing contextual information related to service calls. To address these challenges, we propose a spatio-temporal context-aware QoS prediction method via contrastive learning, which effectively integrates temporal and spatial contextual information of users and services. First, we employ a neighborhood selection method that combines spatial and temporal dimensions to construct embeddings for similar users and services. Second, we generate contrastive views based on the similar and original embeddings, leveraging contrastive learning to capture self-supervised signals and alleviate data sparsity. Finally, the resulting embeddings, which incorporate both neighborhood and context information, are input into a multi-layer perceptron (MLP) for QoS prediction. Extensive experiments on real-world datasets demonstrate the effectiveness of our method, particularly in scenarios with extremely sparse training data, resulting in more accurate QoS predictions.