Matrix Completion in the Unit Hypercube via Structured Matrix Factorization
This addresses a specific problem for VFX companies in optimizing artist efficiency, but it is incremental as it adapts existing matrix completion methods to a bounded domain with domain-specific knowledge.
The paper tackled predicting artist efficiency in visual effects rendering by formulating it as a constrained matrix completion problem with entries in [0,1], and proposed two novel matrix factorization models (EMF and SMF) that showed effectiveness in numerical tests on a VFX dataset and two additional datasets.
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.