{"type":"ScholarlyArticle","title":"HPDM: A Hierarchical Popularity-aware Debiased Modeling Approach for Personalized News Recommender","authors":[{"name":"Xiangfu He","slug":"xiangfu-he","given":"Xiangfu","family":"He","affiliation":""},{"name":"Qiyao Peng","slug":"qiyao-peng","given":"Qiyao","family":"Peng","affiliation":""},{"name":"Minglai Shao","slug":"minglai-shao","given":"Minglai","family":"Shao","affiliation":""},{"name":"Hongtao Liu","slug":"hongtao-liu","given":"Hongtao","family":"Liu","affiliation":""}],"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}.","doi":"10.24963/ijcai.2025/321","url":"https://ijcai.org/proceedings/2025/321","pdf":"https://ijcai.org/proceedings/2025/0321.pdf","markdown":"https://ijcai.org/proceedings/2025/321.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/321","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Data Mining","track":"Main Track","number":321,"pages":{"first":2883,"last":2891},"keywords":["Humans and AI: HAI: Personalization and user modeling","Data Mining: DM: Recommender systems"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}