  Group buying, as an emerging form of purchase in social e-commerce websites,
such as Pinduoduo, has recently achieved great success. In this new business
model, users, initiator, can launch a group and share products to their social
networks, and when there are enough friends, participants, join it, the deal is
clinched. Group-buying recommendation for social e-commerce, which recommends
an item list when users want to launch a group, plays an important role in the
group success ratio and sales. However, designing a personalized recommendation
model for group buying is an entirely new problem that is seldom explored. In
this work, we take the first step to approach the problem of group-buying
recommendation for social e-commerce and develop a GBGCN method (short for
Group-Buying Graph Convolutional Network). Considering there are multiple types
of behaviors (launch and join) and structured social network data, we first
propose to construct directed heterogeneous graphs to represent behavioral data
and social networks. We then develop a graph convolutional network model with
multi-view embedding propagation, which can extract the complicated high-order
graph structure to learn the embeddings. Last, since a failed group-buying
implies rich preferences of the initiator and participants, we design a
double-pairwise loss function to distill such preference signals. We collect a
real-world dataset of group-buying and conduct experiments to evaluate the
performance. Empirical results demonstrate that our proposed GBGCN can
significantly outperform baseline methods by 2.69%-7.36%. The codes and the
dataset are released at
https://github.com/Sweetnow/group-buying-recommendation.
