DySR: A Dynamic Representation Learning and Aligning based Model for Service Bundle Recommendation
This work addresses service bundle recommendation for users needing effective service reuse, but it is incremental as it builds on existing methods with specific enhancements.
The paper tackles the problem of service bundle recommendation by addressing the evolution of services over time and the representation gap between services and requirements, resulting in an improvement of F1@5 from 36.1% to 69.3% on a real-world dataset.
An increasing number and diversity of services are available, which result in significant challenges to effective reuse service during requirement satisfaction. There have been many service bundle recommendation studies and achieved remarkable results. However, there is still plenty of room for improvement in the performance of these methods. The fundamental problem with these studies is that they ignore the evolution of services over time and the representation gap between services and requirements. In this paper, we propose a dynamic representation learning and aligning based model called DySR to tackle these issues. DySR eliminates the representation gap between services and requirements by learning a transformation function and obtains service representations in an evolving social environment through dynamic graph representation learning. Extensive experiments conducted on a real-world dataset from ProgrammableWeb show that DySR outperforms existing state-of-the-art methods in commonly used evaluation metrics, improving $F1@5$ from $36.1\%$ to $69.3\%$.