  Sequential fashion recommendation is of great significance in online fashion
shopping, which accounts for an increasing portion of either fashion retailing
or online e-commerce. The key to building an effective sequential fashion
recommendation model lies in capturing two types of patterns: the personal
fashion preference of users and the transitional relationships between adjacent
items. The two types of patterns are usually related to user-item interaction
and item-item transition modeling respectively. However, due to the large sets
of users and items as well as the sparse historical interactions, it is
difficult to train an effective and efficient sequential fashion recommendation
model. To tackle these problems, we propose to leverage two types of global
graph, i.e., the user-item interaction graph and item-item transition graph, to
obtain enhanced user and item representations by incorporating higher-order
connections over the graphs. In addition, we adopt the graph kernel of LightGCN
for the information propagation in both graphs and propose a new design for
item-item transition graph. Extensive experiments on two established sequential
fashion recommendation datasets validate the effectiveness and efficiency of
our approach.
