{"type":"ScholarlyArticle","title":"Quantifying Aspect Bias in Ordinal Ratings using a Bayesian Approach","authors":[{"name":"Lahari Poddar","slug":"lahari-poddar","given":"Lahari","family":"Poddar","affiliation":"School of Computing , National University of Singapore"},{"name":"Wynne Hsu","slug":"wynne-hsu","given":"Wynne","family":"Hsu","affiliation":"School of Computing , National University of Singapore"},{"name":"Mong Li Lee","slug":"mong-li-lee","given":"Mong Li","family":"Lee","affiliation":"School of Computing , National University of Singapore"}],"abstract":"User opinions expressed in the form of ratings can influence an individual's view of an item.\r\nHowever, the true quality of an item is often obfuscated by user biases, and it is not obvious from the observed ratings\r\nthe  importance different users place on different aspects of an item.\r\nWe propose a probabilistic modeling of the observed aspect ratings to infer (i) each user's aspect bias and (ii) latent intrinsic quality of an item. We model multi-aspect ratings as ordered discrete data and encode the dependency between different aspects by using a latent Gaussian structure. We handle the Gaussian-Categorical non-conjugacy using a stick-breaking formulation coupled with P\\'{o}lya-Gamma auxiliary variable augmentation for a simple, fully Bayesian inference. On two real world datasets, we demonstrate the predictive ability of our model and its effectiveness in learning explainable user biases to provide insights towards a more reliable product quality estimation.","doi":"10.24963/ijcai.2017/530","url":"https://ijcai.org/proceedings/2017/530","pdf":"https://ijcai.org/proceedings/2017/0530.pdf","markdown":"https://ijcai.org/proceedings/2017/530.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/530","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":530,"pages":{"first":3791,"last":3797},"keywords":["Multidisciplinary Topics and Applications: Personalization and User Modeling","Machine Learning: Learning Graphical Models","Uncertainty in AI: Bayesian Networks","Uncertainty in AI: Graphical Models"],"videos":[],"references":[{"position":1,"raw":"[Albert and Chib, 1993] James H Albert and Siddhartha Chib. 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