QoS-Aware Graph Contrastive Learning for Web Service Recommendation

• URL: http://arxiv.org/abs/2401.03162v1
• Date: Sat, 6 Jan 2024 08:36:04 GMT
• Title: QoS-Aware Graph Contrastive Learning for Web Service Recommendation
• Authors: Jeongwhan Choi, Duksan Ryu
• Abstract summary: This study aims to address the challenges of data sparsity and the cold-start problem in web service recommendation using Quality of Service (QoS) Our model harnesses the power of graph contrastive learning to handle cold-start problems and improve accuracy effectively. Our research contributes to the potential for more accurate recommendations in real-world scenarios, even with limited user-service interaction data.
• Score: 3.130026754572506
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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