Game Data Mining Competition on Churn Prediction and Survival Analysis using Commercial Game Log Data

TL;DR

This paper presents an international game data mining competition using real commercial game logs from NCSOFT's Blade & Soul, focusing on predicting player churn and survival analysis to advance AI research in gaming.

Contribution

It introduces a novel open data set from a major game company and demonstrates the application of advanced machine learning techniques in a competitive setting.

Findings

Deep learning and tree boosting were effective in churn prediction.

Open data facilitated research in game data mining.

The competition highlighted the potential of AI techniques in gaming analytics.

Abstract

Game companies avoid sharing their game data with external researchers. Only a few research groups have been granted limited access to game data so far. The reluctance of these companies to make data publicly available limits the wide use and development of data mining techniques and artificial intelligence research specific to the game industry. In this work, we developed and implemented an international competition on game data mining using commercial game log data from one of the major game companies in South Korea: NCSOFT. Our approach enabled researchers to develop and apply state-of-the-art data mining techniques to game log data by making the data open. For the competition, data were collected from Blade & Soul, an action role-playing game, from NCSOFT. The data comprised approximately 100 GB of game logs from 10,000 players. The main aim of the competition was to predict whether a player would churn and when the player would churn during two periods between which the business model was changed to a free-to-play model from a monthly subscription. The results of the competition revealed that highly ranked competitors used deep learning, tree boosting, and linear regression.