- 2017-09-04: Announce of competition results.
2017/07/312017/08/10: Deadline for the final submission
- 2017/05/30: Tutorial of simple model is updated.
- 2017/05/19: Test server (validation server) is opened in Google groups.
- 2017/05/18: Team registration started in Google groups.
- 2017/05/15: The opening date of validation server is delayed.
- 2017/04/30: It is possible to download Log Data and Data Description in Google groups.
- 2017/04/28: The data structure in Rule page is updated.
- 2017/04/25: Sample Log Data is updated. Please check it in Google groups.
- 2017/04/21: Detail of data structure is updated in Rule page.
- 2017/04/15: Introduction page and rules page are added.
- 2017/04/15: It is possible to download Sample Log Data and Log Schema in Google groups.
- 2017/04/12: Evaluation method is updated.
2017/03/28: Game data mining competition 2017 homepage is opened.
2017/03/28: Google Group for this competition is opened. Please register to the group to get news .from us. The data download link will be provided through the Google group channel (Go to Registration Page).
In game analytics field, the game data mining has been recognized as one of the important tools to understand game players’ behaviors. It can help game companies predict players’ churn/retention or purchase behaviors from game log data. Although the game log data mining is so important in game AI community, there are few public datasets available to researchers and it limits the growth of the field. In this competition, participants can access to the big game log data recorded by NCSOFT, one of the biggest game companies in South Korea. This competition is officially approved by IEEE Computational Intelligence and Games 2017 (IEEE CIG 2017).
The goal of this competition is to predict the game players’ engagement to commercial MMORPG game from the massive game log data. Especially, the game has experienced the change of payment policy from fixed charge system to free-to-play. This competition will evaluate entries’ robust performance to make predictions on test datasets.
Track 1: Churn Prediction
In this track, participants will predict players’ churn or retention on the test datasets. The winner will be determined based on the average prediction accuracy.
– Evaluation method:
Track 2: Survival Analysis
In this track, participants will predict the survival time of game players on the test datasets. The winner will be determined based on the average root mean squared error.
– Evaluation method: