Data-driven smoothing approaches for interest modeling in recommendation systems

Denghao Ma, Xiayu Wang, Xueqiang Lv, Hongbin Pei, Liang Shen, Youyou Zhang

Abstract: Highlights•We are the first to apply the smoothing mechanism to bridge interest gaps in the interest modeling task.•We propose two types of data-driven smoothing approaches based on the assumption of collaborative filtering.•A novel iterative Bayesian inference framework on a click-flow graph is designed to search relevant clicks.•New constrained attention networks are proposed to generate high-quality smoothing factors.•Both offline and online experiments show that our models significantly outperform the SOTA baselines.