A Factor-Based Model for Context-Sensitive Skill Rating Systems

Estimating agent´s skill ratings from team competition results has many applications in the real world. Existing models assume skills are the same for all contexts. However, skills are context-sensitive in a variety of cases. In this paper, we present a Factor-Based Context-Sensitive Skill Rating System(FBCS-SRS). Instead of estimating agent skills under every context, which is hard due to data sparisity, we propose a factor model where individual skills are modelled by the inner product of an agent factor vector and a context factor vector. Collapsed Gibbs sampling is used for approximate inference. We formulate the problem of sampling linear constraint factors as a variant of MAX-SAT, and solve it by linear programming algorithms . We validate our model on two real-world datasets. Experiments show that FBCS-SRS achieves significantly higher prediction accuracy than other skill rating systems. The improvement is even more obvious when there are a lot of contexts.