DySR: A Dynamic Representation Learning and Aligning based Model for
  Service Bundle Recommendation
  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 $69.3\%$.
