RecruitView: A Multimodal Dataset for Predicting Personality and Interview Performance for Human Resources Applications

TL;DR

RecruitView introduces a large multimodal dataset of interview videos and a novel geometric deep learning framework, CRMF, for improved personality and interview performance prediction in HR applications.

Contribution

The paper presents RecruitView, a new multimodal dataset, and CRMF, a geometric deep learning model that captures complex behavioral traits across multiple manifolds.

Findings

CRMF outperforms baseline models in predicting personality and interview performance.

The dataset includes 2,011 interview clips with 27,000 judgments across 12 dimensions.

CRMF achieves up to 11.4% improvement in Spearman correlation.

Abstract

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