Individualized Context-Aware Tensor Factorization for Online Games Predictions

TL;DR

This paper introduces NICE, a neural tensor factorization model that personalizes context-aware predictions of player performance and game outcomes in MOBA games, outperforming existing methods.

Contribution

The paper presents a novel neural tensor factorization approach that captures individual behavioral differences in game contexts for improved prediction accuracy.

Findings

Significantly improves prediction of game outcomes and user performance

Effectively models individual differences in contextual behavior

Enhances understanding of player engagement in MOBA games

Abstract

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.