HG-PerCon:用于性格预测的交叉视图对比学习
Meiling Li1, Yangfu Zhu1, Shicheng Li2
1Beijing Key Laboratory of Intelligence Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, PR China.
概括
这项研究引入了HG-PerCon用于人格预测,通过使用历史用户数据和心理知识来提高准确性. 该模型在不同的观点中捕获一致的个性线索,超过现有方法.
科学领域:
- 计算社会科学 计算社会科学
- 心理学信息学 心理学信息学
背景情况:
- 人格预测对于理解心理学和行为经济学等领域的用户行为至关重要.
- 目前的方法主要分析用户帖子或心理数据,但往往忽略了人格的长期一致性和交叉视图线索集成.
研究的目的:
- 开发一种高效有效的个性预测模型,解决现有方法的局限性.
- 为了在不同的用户数据视图中捕获一致的,持久的与人格相关的信息.
主要方法:
- 拟议的HG-PerCon模型利用历史语义信息和心理知识的用户表示.
- 使用基于变压器的模块从用户帖子中提取长期的个性信息.
- 利用心理知识图表与语言风格用于以知识为导向的用户表示.
- 利用交叉视图对比学习来确保人格线索的一致性.
主要成果:
- HG-PerCon模型在人格预测方面表现出卓越的表现.
- 与基线方法相比,实现了根平均平方误差 (RMSE) 的显著降低,改进率为2%,4%和6%.
结论:
- HG-PerCon有效地整合了历史语义数据和心理知识,以进行强大的个性预测.
- 交叉视图对比学习方法增强了随着时间的推移捕捉一致的个性特征.
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