Individualized Context-Aware Tensor Factorization for Online Games
  Predictions
  Individual behavior and decisions are substantially influenced by their
contexts, such as location, environment, and time. Changes along these
dimensions can be readily observed in Multiplayer Online Battle Arena games
(MOBA), where players face different in-game settings for each match and are
subject to frequent game patches. Existing methods utilizing contextual
information generalize the effect of a context over the entire population, but
contextual information tailored to each individual can be more effective. To
achieve this, we present the Neural Individualized Context-aware Embeddings
(NICE) model for predicting user performance and game outcomes. Our proposed
method identifies individual behavioral differences in different contexts by
learning latent representations of users and contexts through non-negative
tensor factorization. Using a dataset from the MOBA game League of Legends, we
demonstrate that our model substantially improves the prediction of winning
outcome, individual user performance, and user engagement.
