  In online shopping, ever-changing fashion trends make merchants need to
prepare more differentiated products to meet the diversified demands, and
e-commerce platforms need to capture the market trend with a prophetic vision.
For the trend prediction, the attribute tags, as the essential description of
items, can genuinely reflect the decision basis of consumers. However, few
existing works explore the attribute trend in the specific community for
e-commerce. In this paper, we focus on the community trend prediction on the
item attribute and propose a unified framework that combines the dynamic
evolution of two graph patterns to predict the attribute trend in a specific
community. Specifically, we first design a communityattribute bipartite graph
at each time step to learn the collaboration of different communities. Next, we
transform the bipartite graph into a hypergraph to exploit the associations of
different attribute tags in one community. Lastly, we introduce a dynamic
evolution component based on the recurrent neural networks to capture the
fashion trend of attribute tags. Extensive experiments on three real-world
datasets in a large e-commerce platform show the superiority of the proposed
approach over several strong alternatives and demonstrate the ability to
discover the community trend in advance.
