SkillSF: In the Sizing Game, Your Size is Your Skill

Rating systems are popular in the gaming industry to maximize the uncertainty of the win/loss outcome of future games by pairing players with similar skills thanks to ranking players through a score representing their “true” skill according to their performance in past games. Inspired by this approach, we propose to tackle the challenging problem of online size recommendations in fashion by reformulating a customer’s purchases as multiple sizing games where we denote (a) the return status of an article as a win/loss (size-related return) or as a draw (no size-related return) and (b) the garment/body dimensions or size as the skill. This re-framing allows us to leverage rating systems such as TrueSkill [1] and to propagate semantically meaningful information between article sizes and customer sizes. Through experimentations with real-life data, we demonstrate that our approach SkillSF is competitive with the state-of-the-art size recommendation models and offers valuable insights as to the underlying true size of customers and articles.