网络游戏案例研究:用户行为分析和流失预测

User Behavior Analysis and Churn Prediction:A Case Study on Online Games

用户流失预测在很多领域得到关注,目前主流的用户流失预测方法是使用分类法。网络游戏领域发展迅猛,但用户特征选取、特征处理和流失预测的相关研究较少。本文以一款网页网络游戏的用户记录为数据,对用户游戏行为进行分析对比,发现流失用户在游戏投入、博彩热情、玩家互动方面与正常用户存在显著差异;同时发现网络游戏数据存在样本分布不平衡、候选特征库庞大和干扰差异多等难点。在此分析基础上,本文探讨了网游用户的关键特征提取的关注方向,以及归一化和对齐化在特征处理中的关键作用。实验表明,本文提取的特征具有很好的区分度。

The task of user churn prediction is a research issue in many fields.Currently the available solution usually built uopna classification models.For the online games which is developing rapidly,the churn prediction is not well addressed yet.This paper chooses certain online game user logs and analyzed user behaviors,finding significant differences in game investment,interests in lottery and player interaction between churn users and normal users.This paper also suggests that there are such challenges in online game data processing as the unbalanced data,the huge candidate features,the interference differences and so on.This paper also discusses the direction when selecting features,as well as the key role of normalization and alignment in feature processing.Experiments prove that the features selected by this paper are informative.