{"type":"ScholarlyArticle","title":"Adversarial Propensity Weighting for Debiasing in Collaborative Filtering","authors":[{"name":"Kuiyu Zhu","slug":"kuiyu-zhu","given":"Kuiyu","family":"Zhu","affiliation":""},{"name":"Tao Qin","slug":"tao-qin","given":"Tao","family":"Qin","affiliation":""},{"name":"Pinghui Wang","slug":"pinghui-wang","given":"Pinghui","family":"Wang","affiliation":""},{"name":"Xin Wang","slug":"xin-wang","given":"Xin","family":"Wang","affiliation":""}],"abstract":"Debiased recommendation focuses on alleviating the negative impact of various biases on recommendation quality to achieve fairer personalized recommendations. Current research mainly relies on propensity score estimation or causal inference methods to alleviate selection bias; at the same time, research on prevalence bias has proposed a variety of methods based on causal graphs and contrastive learning. However, these methods have shortcomings in dealing with unstable propensity score estimates, bias interactions, and decoupling of interest and bias signals, which limits the performance improvement of recommender systems. To this end, this paper proposes APWCF, a collaborative filtering debiased method that combines dynamic propensity modeling and adversarial learning. APWCF solves the problem of high variance in propensity scores through the dynamic propensity factor, and decouples user interests and bias signals through the adversarial learning to effectively remove multiple biases. Experiments show that APWCF significantly outperforms existing methods across various benchmark datasets from different domains. Compared with the current optimal baseline PDA, Recall@10 and NDCG@10 improve by 0.10%-5.42% and 1.01%-8.60% respectively.","doi":"10.24963/ijcai.2025/412","url":"https://ijcai.org/proceedings/2025/412","pdf":"https://ijcai.org/proceedings/2025/0412.pdf","markdown":"https://ijcai.org/proceedings/2025/412.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/412","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":412,"pages":{"first":3707,"last":3715},"keywords":["Data Mining: DM: Collaborative filtering","Data Mining: DM: Recommender systems"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}