{"type":"ScholarlyArticle","title":"Towards Resolving Propensity Contradiction in Offline Recommender Learning","authors":[{"name":"Yuta Saito","slug":"yuta-saito","given":"Yuta","family":"Saito","affiliation":"Cornell University"},{"name":"Masahiro Nomura","slug":"masahiro-nomura","given":"Masahiro","family":"Nomura","affiliation":"CyberAgent , Inc."}],"abstract":"We study offline recommender learning from explicit rating feedback in the presence of selection bias. A current promising solution for dealing with the bias is the inverse propensity score (IPS) estimation. However, the existing propensity-based methods can suffer significantly from the propensity estimation bias. In fact, most of the previous IPS-based methods require some amount of missing-completely-at-random (MCAR) data to accurately estimate the propensity. This leads to a critical self-contradiction; IPS is ineffective without MCAR data, even though it originally aims to learn recommenders from only missing-not-at-random feedback. To resolve this propensity contradiction, we derive a propensity-independent generalization error bound and propose a novel algorithm to minimize the theoretical bound via adversarial learning. Our theory and algorithm do not require a propensity estimation procedure, thereby leading to a well-performing rating predictor without the true propensity information. Extensive experiments demonstrate that the proposed algorithm is superior to a range of existing methods both in rating prediction and ranking metrics in practical settings without MCAR data. Full version of the paper (including the appendix) is available at: https://arxiv.org/abs/1910.07295.","doi":"10.24963/ijcai.2022/307","url":"https://ijcai.org/proceedings/2022/307","pdf":"https://ijcai.org/proceedings/2022/0307.pdf","markdown":"https://ijcai.org/proceedings/2022/307.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/307","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Data Mining","track":"Main Track","number":307,"pages":{"first":2211,"last":2217},"keywords":["Data Mining: Recommender Systems","Data Mining: Information Retrieval","Machine Learning: Recommender Systems","Data Mining: Theoretical Foundations of Data Mining","Data Mining: Collaborative Filtering"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986957","length_sec":89,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985100","length_sec":709,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Ben-David et al., 2010] Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jen- nifer Wortman Vaughan. 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