  With the rapid growth of cloud services driven by advancements in web service
technology, selecting a high-quality service from a wide range of options has
become a complex task. This study aims to address the challenges of data
sparsity and the cold-start problem in web service recommendation using Quality
of Service (QoS). We propose a novel approach called QoS-aware graph
contrastive learning (QAGCL) for web service recommendation. Our model
harnesses the power of graph contrastive learning to handle cold-start problems
and improve recommendation accuracy effectively. By constructing contextually
augmented graphs with geolocation information and randomness, our model
provides diverse views. Through the use of graph convolutional networks and
graph contrastive learning techniques, we learn user and service embeddings
from these augmented graphs. The learned embeddings are then utilized to
seamlessly integrate QoS considerations into the recommendation process.
Experimental results demonstrate the superiority of our QAGCL model over
several existing models, highlighting its effectiveness in addressing data
sparsity and the cold-start problem in QoS-aware service recommendations. Our
research contributes to the potential for more accurate recommendations in
real-world scenarios, even with limited user-service interaction data.
