{"type":"ScholarlyArticle","title":"Matrix Completion in the Unit Hypercube via Structured Matrix Factorization","authors":[{"name":"Emanuele Bugliarello","slug":"emanuele-bugliarello","given":"Emanuele","family":"Bugliarello","affiliation":"Tokyo Institute of Technology"},{"name":"Swayambhoo Jain","slug":"swayambhoo-jain","given":"Swayambhoo","family":"Jain","affiliation":"Technicolor AI Lab"},{"name":"Vineeth Rakesh","slug":"vineeth-rakesh","given":"Vineeth","family":"Rakesh","affiliation":"Technicolor AI Lab"}],"abstract":"Several complex tasks that arise in organizations can be simplified by mapping them into a matrix completion problem. In this paper, we address a key challenge faced by our company: predicting the efficiency of artists in rendering visual effects (VFX) in film shots. We tackle this challenge by using a two-fold approach: first, we transform this task into a constrained matrix completion problem with entries bounded in the unit interval [0,1]; second, we propose two novel matrix factorization models that leverage our knowledge of the VFX environment. Our first approach, expertise matrix factorization (EMF), is an interpretable method that structures the latent factors as weighted user-item interplay. The second one, survival matrix factorization (SMF), is instead a probabilistic model for the underlying process defining employees' efficiencies. We show the effectiveness of our proposed models by extensive numerical tests on our VFX dataset and two additional datasets with values that are also bounded in the [0,1] interval.","doi":"10.24963/ijcai.2019/282","url":"https://ijcai.org/proceedings/2019/282","pdf":"https://ijcai.org/proceedings/2019/0282.pdf","markdown":"https://ijcai.org/proceedings/2019/282.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/282","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning A-L","track":"Main track","number":282,"pages":{"first":2038,"last":2044},"keywords":["Machine Learning: Probabilistic Machine Learning","Machine Learning: Recommender Systems"],"videos":[],"references":[{"position":1,"raw":"[Aharon et al., 2006] Michal Aharon, Michael Elad, Alfred Bruckstein, et al. 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