# Individualized Context-Aware Tensor Factorization for Online Games Predictions

arXiv: https://arxiv.org/abs/2102.11352 · HTML: https://mlsanity.com/paper/2102.11352/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 22 Feb 2021.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Deep Neural Networks for Optimal Team Composition](https://mlsanity.com/paper/1805.03285/) (2018)
- [Non-negative Tensor Factorization for Human Behavioral Pattern Mining in Online Games](https://mlsanity.com/paper/1702.05695/) (2017)
- [Clustering Patients with Tensor Decomposition](https://mlsanity.com/paper/1708.08994/) (2017)
- [Tensor Embedding: A Supervised Framework for Human Behavioral Data Mining and Prediction](https://mlsanity.com/paper/1808.10867/) (2018)
- [Discovering Hidden Structure in High Dimensional Human Behavioral Data via Tensor Factorization](https://mlsanity.com/paper/1905.08846/) (2019)

## Built on by (most cited first, 1 of 1)

- [Voice Communication Analysis in Esports](https://mlsanity.com/paper/2411.19793/) (2024)

## Abstract (arXiv)

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.