构建首个用于招聘评估的多模态数据集，提升性格与面试表现预测准确率
RecruitView: A Multimodal Dataset for Predicting Personality and Interview Performance for Human Resources Applications
- 基于几何深度学习，融合双曲、球面与欧氏空间建模行为特征
- 在12维度上实现最高11.4%的斯皮尔曼相关性提升，参数量减少40%-50%
- 适合人机交互、HR智能评估、多模态行为分析研究者使用
自动化从多模态行为数据中评估性格与软技能仍面临挑战，主要受限于数据集匮乏及模型难以捕捉人类特质中的几何结构。我们提出RecruitView，一个包含300+参与者、2,011段自然对话视频采访片段的数据集，涵盖27,000次成对比较判断，覆盖12个维度：五大性格特质、总体性格评分以及六项面试表现指标。为充分利用该数据，我们提出跨模态流形融合回归（CRMF），一种显式建模超曲面、球面与欧氏空间行为表示的几何深度学习框架。CRMF采用几何专用专家网络，同步捕捉层级特质结构、方向性行为模式与连续表现变化，并通过自适应路由机制动态加权专家贡献。通过规范的切空间融合策略，CRMF在训练参数减少40%-50%的情况下仍取得更优性能。大量实验表明，相比基线模型，CRMF显著提升预测效果，斯皮尔曼相关性最高提升11.4%，一致性指数提升6.0%。RecruitView数据集已公开发布于https://huggingface.co/datasets/AI4A-lab/RecruitView。
原文摘要 · Abstract (English)
Automated personality and soft skill assessment from multimodal behavioral data remains challenging due to limited datasets and methods that fail to capture geometric structure inherent in human traits. We introduce RecruitView, a dataset of 2,011 naturalistic video interview clips from 300+ participants with 27,000 pairwise comparative judgments across 12 dimensions: Big Five personality traits, overall personality score, and six interview performance metrics. To leverage this data, we propose Cross-Modal Regression with Manifold Fusion (CRMF), a geometric deep learning framework that explicitly models behavioral representations across hyperbolic, spherical, and Euclidean manifolds. CRMF employs geometry-specific expert networks to capture hierarchical trait structures, directional behavioral patterns, and continuous performance variations simultaneously. An adaptive routing mechanism dynamically weights expert contributions based on input characteristics. Through principled tangent space fusion, CRMF achieves superior performance while training 40-50% fewer trainable parameters than large multimodal models. Extensive experiments demonstrate that CRMF substantially outperforms the selected baselines, achieving up to 11.4% improvement in Spearman correlation and 6.0% in concordance index. Our RecruitView dataset is publicly available at https://huggingface.co/datasets/AI4A-lab/RecruitView
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