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An increasing number and diversity of services are available, which result insignificant challenges to effective reuse service during requirementsatisfaction. There have been many service bundle recommendation studies andachieved remarkable results. However, there is still plenty of room forimprovement in the performance of these methods. The fundamental problem withthese studies is that they ignore the evolution of services over time and therepresentation gap between services and requirements. In this paper, we proposea dynamic representation learning and aligning based model called DySR totackle these issues. DySR eliminates the representation gap between servicesand requirements by learning a transformation function and obtains servicerepresentations in an evolving social environment through dynamic graphrepresentation learning. Extensive experiments conducted on a real-worlddataset from ProgrammableWeb show that DySR outperforms existingstate-of-the-art methods in commonly used evaluation metrics, improving $F1@5$from $36.1\%$ to $69.3\%$.

DySR: A Dynamic Representation Learning and Aligning based Model for Service Bundle Recommendation