  Recently, a new form of online shopping becomes more and more popular, which
combines live streaming with E-Commerce activity. The streamers introduce
products and interact with their audiences, and hence greatly improve the
performance of selling products. Despite of the successful applications in
industries, the live stream E-commerce has not been well studied in the data
science community. To fill this gap, we investigate this brand-new scenario and
collect a real-world Live Stream E-Commerce (LSEC) dataset. Different from
conventional E-commerce activities, the streamers play a pivotal role in the
LSEC events. Hence, the key is to make full use of rich interaction information
among streamers, users, and products. We first conduct data analysis on the
tripartite interaction data and quantify the streamer's influence on users'
purchase behavior. Based on the analysis results, we model the tripartite
information as a heterogeneous graph, which can be decomposed to multiple
bipartite graphs in order to better capture the influence. We propose a novel
Live Stream E-Commerce Graph Neural Network framework (LSEC-GNN) to learn the
node representations of each bipartite graph, and further design a multi-task
learning approach to improve product recommendation. Extensive experiments on
two real-world datasets with different scales show that our method can
significantly outperform various baseline approaches.
