Reviews: Learning from Group Comparisons: Exploiting Higher Order Interactions

Summary: This paper develops a model that can capture player-interactions from group comparisons (team-play win/loss info). In an effort to address higher-order interactions with a reasonable size of data set, it then proposes a latent factor model and the sample complexity analysis for the model is done under certain scenarios. Experiments are conducted on real-world on-line game datasets, comparing the win/loss prediction accuracy of the proposed approach to the prior methods such as BTL [12] and Trueskill [11]. Detailed comments: The paper studies an interesting problem, and investigates the role of player-interactions which has been out of reach in the literature. One noticeable observation found in the paper is that the proposed approach may be able to identify the best team members with good chemistry, as suggested in Table 3.