一个改进的图形因子化机器,基于解决不平衡的游戏感知
Xiaoxia Xie1, Yuan Jia2, Tiande Ma3
1BYD Company Limited, Shenzhen, China.
Frontiers in neurorobotics
|December 19, 2024
概括
本研究介绍了平衡图形因子化机器 (BGFM) 以改进移动游戏用户感知模型. BGFM有效地处理稀疏的数据和不平衡的类别,大大提高了预测准确度.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 人与计算机的交互
背景情况:
- 用户感知对于移动游戏用户体验和利能力至关重要.
- 稀少的游戏数据和不平衡的类别挑战了预测模型的性能.
- 现有的模型在高维特征和数据异质性方面扎.
研究的目的:
- 提出一种新的方法,即平衡图形分解机 (BGFM),以解决移动游戏感知建模中的数据不平衡和高维特征.
- 提高用户体验预测的准确性,从而提高游戏的利能力.
主要方法:
- 使用边界线-SMOTE过量抽样来解决类别不平衡的数据平衡.
- 图形结构的特征表示,以自然捕捉特征关系.
- 将因子化机器 (FM) 和图形神经网络 (GNN) 与顺序特征交互分析的注意力机制集成.
主要成果:
- 与八个最先进的模型相比,拟议的BGFM方法表现出优越的性能.
- 在关键指标上观察到显著的改善:曲线下的面积 (AUC),精度,回忆和F-测量.
- 通过基于图形的交互机制,BGFM有效地汇总了有益的特征.
结论:
- 平衡图形因子化机器 (BGFM) 为模拟移动游戏中的用户感知提供了强大的解决方案,特别是在稀疏和不平衡的数据中.
- 混合FM-GNN方法与注意力机制增强了捕捉复杂特征相互作用的能力.
- BGFM显著优于现有方法,为改进用户体验和游戏货币化策略铺平了道路.
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