使用商业游戏日志数据进行流失预测和生存分析的游戏数据数据采掘竞赛
Game Data Mining Competition on Churn Prediction and Survival Analysis using Commercial Game Log Data
摘要：
游戏公司避免与外部研究人员分享他们的游戏数据。到目前为止，只有少数研究小组被授予有限的游戏数据访问权。这些公司不愿公开数据，限制了数据采掘技术和人工智能研究在游戏行业的广泛应用和发展。在这项工作中，我们开发和实施了一个国际比赛的游戏数据采掘使用商业游戏日志数据从一个主要的游戏公司在韩国：NCSOFT。我们的方法使研究人员能够开发和应用国家的最先进的数据采掘技术的游戏日志数据，使数据开放。对于比赛，数据收集自NCSOFT的动作角色扮演游戏Blade & Soul。这些数据包括来自10，000名玩家的约100 GB游戏日志。比赛的主要目的是预测玩家是否会流失，以及在商业模式从包月改为免费模式的两个时期内玩家何时流失。比赛结果显示，排名靠前的竞争对手使用了深度学习、树提升和线性回归。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
