---
title: "HPDM: A Hierarchical Popularity-aware Debiased Modeling Approach for Personalized News Recommender"
authors:
  - "Xiangfu He"
  - "Qiyao Peng"
  - "Minglai Shao"
  - "Hongtao Liu"
doi: "10.24963/ijcai.2025/321"
url: "https://ijcai.org/proceedings/2025/321"
pdf: "https://ijcai.org/proceedings/2025/0321.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "2883-2891"
track: "Main Track"
keywords:
  - "Humans and AI: HAI: Personalization and user modeling"
  - "Data Mining: DM: Recommender systems"
---

# HPDM: A Hierarchical Popularity-aware Debiased Modeling Approach for Personalized News Recommender

Xiangfu He, Qiyao Peng, Minglai Shao, Hongtao Liu

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 2883-2891. DOI: [10.24963/ijcai.2025/321](https://doi.org/10.24963/ijcai.2025/321)

## Abstract

News recommender systems face inherent challenges from popularity bias, where user interactions concentrate heavily on a small subset of popular news. While existing debiasing methods have made progress in recommendation, they often overlook two critical aspects: the different granularity of news popularity (across titles, categories, etc.) and how hierarchical popularity levels distinctly influence user interest modeling. Hence, in this paper, we propose a hierarchical causal debiasing framework that effectively captures genuine user interests while mitigating popularity bias at different granularity levels. Our framework incorporates two key components during training: (1) a hierarchical popularity-aware user modeling module to capture user interests by distinguishing popular and unpopular interactions at different granularity news content; and (2) a dual-view structure combining counterfactual reasoning for popular-view news with inverse propensity weighting for unpopular-view news to model user genuine interests. During inference, our framework removes popularity-induced effects to predict relatedness between user and candidate news. Extensive experiments on two widely-used datasets, MIND and Adressa, demonstrate that our framework significantly outperforms existing baseline approaches in addressing both the long-tail distribution challenge. Our code is available at \url{https://github.com/hexiangfu123/HPDM}.