关键词: 分类算法, 游戏行会, 联合模型, 生存预测

Abstract: Guild survival has a positive effect on improving the game user’s activity and retention rate. Currently the main approach to the problem is based on a two-class classification process, which fails to make full use of the longitudinal data reflecting the state change and the survival trend of a guild. A joint model for longitudinal and survival data is adopted to predict guild survival state. The model fully utilizes the features of the attributions and member behaviors for a guild. The experiments show that the performance of the joint model for the longitudinal and survival data improves the overall performances of 56.6% than Cox proportional hazards model, and the model is more precise than that of the commonly used classification algorithms, e. g. the overall performance of this approach is 11. 9% better than that of logistic regression. Moreover, the following conclusions can be drawn from the experiment. The first is that a more hierarchical guild architecture leads to a more stable guild, since the standard deviation of the right levels of the members has a positive impact on the guild survival. The second is that, the diversity among the behavior of guild members results in longer guild survival time, according to the effects of the standard deviation of private chat times and the PK times. The third is that, the survival time for guild shows a negative effect, and this means the longer the guild is living, the less conducive to the survival of the guild.

Key words: classification algorithms, game guild, joint model, survival prediction