{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T21:16:05Z","timestamp":1773263765816,"version":"3.50.1"},"reference-count":65,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T00:00:00Z","timestamp":1762905600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T00:00:00Z","timestamp":1762905600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,12]]},"DOI":"10.1109\/icdmw69685.2025.00289","type":"proceedings-article","created":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T19:50:39Z","timestamp":1773172239000},"page":"2365-2371","source":"Crossref","is-referenced-by-count":0,"title":["SemiSL-RDC: Semi-Supervised Rating Distribution Calibration via Pivot User for Debiasing Recommendation Systems"],"prefix":"10.1109","author":[{"given":"Xiaxin","family":"Yuan","sequence":"first","affiliation":[{"name":"University of Science and Technology of China,Hefei,China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25562"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3564284"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240355"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3612591"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3594245"},{"key":"ref6","article-title":"Counterfactual implicit feedback modeling","author":"Zhou","year":"2025","journal-title":"in Advances in Neural Information Processing Systems"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3711896.3736832"},{"key":"ref8","article-title":"Unveiling extraneous sampling bias with data missing-not-at-random","author":"Zheng","year":"2025","journal-title":"in Advances in Neural Information Processing Systems"},{"key":"ref9","article-title":"Open bandit dataset and pipeline: Towards realistic and reproducible off-policy evaluation","author":"Saito","year":"2020","journal-title":"arXiv preprint"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/787"},{"key":"ref11","article-title":"Debiased collaborative filtering with kernel-based causal balancing","volume-title":"International Conference on Learning Representations","author":"Li","year":"2024"},{"key":"ref12","article-title":"Be aware of the neighborhood effect: Modeling selection bias under interference for recommendation","volume-title":"International Conference on Learning Representations","author":"Li","year":"2024"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671915"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/1639714.1639717"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE60146.2024.00056"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159687"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3291027"},{"key":"ref18","article-title":"Self-supervised learning for alleviating selection bias in recommendation systems","volume-title":"in International Workshop on Industrial Recommendation Systems","author":"Liu","year":"2021"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512078"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835895"},{"key":"ref21","article-title":"Probabilistic matrix factorization with non-random missing data","volume-title":"International Conference on Machine Learning","author":"Hern\u00e1ndez-Lobato","year":"2014"},{"key":"ref22","article-title":"Recommendations as treatments: Debiasing learning and evaluation","volume-title":"International Conference on Machine Learning","author":"Schnabel","year":"2016"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/2740908.2742564"},{"key":"ref24","article-title":"Doubly robust joint learning for recommendation on data missing not at random","volume-title":"International Conference on Machine Learning","author":"Wang","year":"2019"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3412262"},{"key":"ref26","article-title":"TDR-CL: Targeted doubly robust collaborative learning for debiased recommendations","volume-title":"International Conference on Learning Representations","author":"Li","year":"2023"},{"key":"ref27","article-title":"Stabledr: Stabilized doubly robust learning for recommendation on data missing not at random","volume-title":"International Conference on Learning Representations","author":"Li","year":"2023"},{"key":"ref28","article-title":"Relaxing the accurate imputation assumption in doubly robust learning for debiased collaborative filtering","volume-title":"International Conference on Machine Learning","author":"Li","year":"2024"},{"key":"ref29","article-title":"CBPL: A unified calibration and balancing propensity learning framework in causal recommendation for debiasing","volume-title":"International Joint Conference on Artificial Intelligence Workshop on Causal Learning for Recommendation Systems","author":"Zhang","year":"2025"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i18.30071"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/3726302.3729887"},{"key":"ref32","article-title":"Collaborative filtering and the missing at random assumption","volume-title":"Conference on Uncertainty in Artificial Intelligence","author":"Marlin","year":"2007"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3614835"},{"key":"ref34","first-page":"27 645","article-title":"Identifiable generative models for missing not at random data imputation","volume":"34","author":"Ma","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583495"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.52202\/075280-2380"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.52202\/079017-4142"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52734.2025.02675"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52734.2025.02187"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3696410.3714482"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599550"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/3690624.3709161"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2024.3516584"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.52202\/075280-0237"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645331"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2024.3493071"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671960"},{"key":"ref48","article-title":"Effective and efficient time-varying counterfactual prediction with statespace models","volume-title":"International Conference on Learning Representations","author":"Wang","year":"2025"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109126"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1162\/089976603321780272"},{"key":"ref51","article-title":"Entropy estimates from insufficient samplings","author":"Grassberger","year":"2003","journal-title":"arXiv preprint physics\/0307138"},{"key":"ref52","article-title":"Propensity matters: Measuring and enhancing balancing for recommendation","volume-title":"International Conference on Machine Learning","author":"Li","year":"2023"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3614805"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1145\/3673762"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.52202\/079017-3305"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657749"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371783"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539240"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210104"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380037"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531972"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240360"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401083"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462919"}],"event":{"name":"2025 IEEE International Conference on Data Mining Workshops (ICDMW)","location":"Washington, DC, USA","start":{"date-parts":[[2025,11,12]]},"end":{"date-parts":[[2025,11,15]]}},"container-title":["2025 IEEE International Conference on Data Mining Workshops (ICDMW)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11415623\/11415713\/11415781.pdf?arnumber=11415781","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T05:15:56Z","timestamp":1773206156000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11415781\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,12]]},"references-count":65,"URL":"https:\/\/doi.org\/10.1109\/icdmw69685.2025.00289","relation":{},"subject":[],"published":{"date-parts":[[2025,11,12]]}}}