Who is the Rising Star? Demystifying the Promising Streamers in Crowdsourced Live Streaming

Abstract

Streamers are the core competency of the crowd-sourced live streaming (CLS) platform. However, little work has explored how different factors relate to their popularity evolution patterns. In this paper, we will investigate a critical problem, i.e., how to discover the promising streamers in their early stage? To tackle this problem, we first conduct large-scale measurement on a real-world CLS dataset. We find that streamers can indeed be clustered into two evolution types (i.e., rising type and normal type), and these two types of streamers will show differences in some inherent properties. Traditional time-sequential models cannot handle this problem, because they are unable to capture the complicated interactivity and extensive heterogeneity in CLS scenarios. To address their shortcomings, we further propose Niffler, a novel heterogeneous attention temporal graph framework (HATG) for predicting the evolution types of CLS streamers. Specifically, through the graph neural network (GNN) and gated-recurrent-unit (GRU) structure, Niffler can capture both the interactive features and the evolutionary dynamics. Moreover, by integrating the attention mechanism in the model design, Niffler can intelligently preserve the heterogeneity when learning different levels of node representations. We systematically compare Niffler against multiple baselines from different categories, and the experimental results show that our proposed model can achieve the best prediction performance.