Multi-Dimensional Prediction of Guild Health in Online Games: A Stability-Aware Multi-Task Learning Approach
Keywords:Data Mining & Knowledge Management (DMKM)
AbstractGuild is the most important long-term virtual community and emotional bond in massively multiplayer online role-playing games (MMORPGs). It matters a lot to the player retention and game ecology how the guilds are going, e.g., healthy or not. The main challenge now is to characterize and predict the guild health in a quantitative, dynamic, and multi-dimensional manner based on complicated multi-media data streams. To this end, we propose a novel framework, namely Stability-Aware Multi-task Learning Approach(SAMLA) to address these challenges. Specifically, different media-specific modules are designed to extract information from multiple media types of tabular data, time seriescharacteristics, and heterogeneous graphs. To capture the dynamics of guild health, we introduce a representation encoder to provide a time series view of multi-media data that is used for task prediction. Inspiredby well-received theories on organization management, we delicately define five specific and quantitative dimensions of guild health and make parallel predictions based on a multi-task approach. Besides, we devise a novel auxiliary task, i.e.,the guild stability, to boost the performance of the guild health prediction task. Extensive experiments on a real-world large-scale MMORPG dataset verify that our proposed method outperforms the state-of-the-art methods in the task of organizational health characterization and prediction. Moreover, our work has been practically deployed in online MMORPG, and case studies clearly illustrate the significant value.
How to Cite
Zhao, C., Zhao, H., Wu, R., Deng, Q., Ding, Y., Tao, J., & Fan, C. (2022). Multi-Dimensional Prediction of Guild Health in Online Games: A Stability-Aware Multi-Task Learning Approach. Proceedings of the AAAI Conference on Artificial Intelligence, 36(4), 4371-4378. https://doi.org/10.1609/aaai.v36i4.20358
AAAI Technical Track on Data Mining and Knowledge Management