  Finding items with potential to increase sales is of great importance in
online market. In this paper, we propose to study this novel and practical
problem: rising star prediction. We call these potential items Rising Star,
which implies their ability to rise from low-turnover items to best-sellers in
the future. Rising stars can be used to help with unfair recommendation in
e-commerce platform, balance supply and demand to benefit the retailers and
allocate marketing resources rationally. Although the study of rising star can
bring great benefits, it also poses challenges to us. The sales trend of rising
star fluctuates sharply in the short-term and exhibits more contingency caused
by some external events (e.g., COVID-19 caused increasing purchase of the face
mask) than other items, which cannot be solved by existing sales prediction
methods. To address above challenges, in this paper, we observe that the
presence of rising stars is closely correlated with the early diffusion of user
interest in social networks, which is validated in the case of Taocode (an
intermediary that diffuses user interest in Taobao). Thus, we propose a novel
framework, RiseNet, to incorporate the user interest diffusion process with the
item dynamic features to effectively predict rising stars. Specifically, we
adopt a coupled mechanism to capture the dynamic interplay between items and
user interest, and a special designed GNN based framework to quantify user
interest. Our experimental results on large-scale real-world datasets provided
by Taobao demonstrate the effectiveness of our proposed framework.
