# Profiling Players with Engagement Predictions

arXiv: https://arxiv.org/abs/1907.03870 · HTML: https://mlsanity.com/paper/1907.03870/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2019.

## Built on by (most cited first, 2 of 2)

- [Midwifery Learning and Forecasting: Predicting Content Demand with User-Generated Logs](https://mlsanity.com/paper/2107.02480/) (2021)
- [Contrastive Multi-view Framework for Customer Lifetime Value Prediction](https://mlsanity.com/paper/2306.14400/) (2023)

## Abstract (arXiv)

The possibility of using player engagement predictions to profile high spending video game users is explored. In particular, individual-player survival curves in terms of days after first login, game level reached and accumulated playtime are used to classify players into different groups. Lifetime value predictions for each player---generated using a deep learning method based on long short-term memory---are also included in the analysis, and the relations between all these variables are thoroughly investigated. Our results suggest this constitutes a promising approach to user profiling.