Mitigating Hidden Confounding Effects for Causal Recommendation

no code implementations • 16 May 2022 • Xinyuan Zhu, Yang Zhang, Fuli Feng, Xun Yang, Dingxian Wang, Xiangnan He

Towards this goal, we propose a Hidden Confounder Removal (HCR) framework that leverages front-door adjustment to decompose the causal effect into two partial effects, according to the mediators between item features and user feedback.

Multi-Task Learning Recommendation Systems