{"type":"ScholarlyArticle","title":"Collaborative Rating Allocation","authors":[{"name":"Yali Du","slug":"yali-du","given":"Yali","family":"Du","affiliation":"† Center for Artificial Intelligence , FEIT , University of Technology Sydney"},{"name":"Chang Xu","slug":"chang-xu","given":"Chang","family":"Xu","affiliation":"‡ UBTech Sydney AI Institute , The School of IT , FEIT , The University of Sydney"},{"name":"Dacheng Tao","slug":"dacheng-tao","given":"Dacheng","family":"Tao","affiliation":"‡ UBTech Sydney AI Institute , The School of IT , FEIT , The University of Sydney"}],"abstract":"This paper studies the collaborative rating allocation problem, in which each user has limited ratings on all items. These users are termed ``energy limited''. Different from existing methods which treat each rating independently, we investigate the geometric properties of a user's rating vector, and design a matrix completion method on the simplex. In this method, a user's rating vector is estimated by the combination of user profiles as basis points on the simplex. Instead of using Euclidean metric, a non-linear pull-back distance measurement from the sphere is adopted since it can depict the geometric constraints on each user's rating vector. The resulting objective function is then efficiently optimized by a Riemannian conjugate gradient method on the simplex. Experiments on real-world data sets demonstrate our model's competitiveness versus other collaborative rating prediction methods.","doi":"10.24963/ijcai.2017/224","url":"https://ijcai.org/proceedings/2017/224","pdf":"https://ijcai.org/proceedings/2017/0224.pdf","markdown":"https://ijcai.org/proceedings/2017/224.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/224","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":"Machine Learning A-R","track":"Main track","number":224,"pages":{"first":1617,"last":1623},"keywords":["Machine Learning: Feature Selection/Construction","Machine Learning: Machine Learning","Machine Learning: Learning Preferences or Rankings"],"videos":[],"references":[{"position":1,"raw":"[Absil et al., 2009] P-A Absil, Robert Mahony, and Rodolphe Sepulchre. 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