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.