Explainable person-job recommendations: challenges, approaches, and comparative analysis.

ABSTRACT:

Introduction

As person-job recommendation systems (PJRS) increasingly mediate hiring decisions, concerns over their "black box" opacity have sparked demand for explainable AI (XAI) solutions.

Methods

This systematic review examines 85 studies on explainable PJRS methods published between 2019 and August 2025, selected from 150 screened articles across Google Scholar, Web of Science, and CNKI, following PRISMA 2020 guidelines.

Results

Guided by a PICOS-formulated review question, we categorize explainability techniques into three layers-data (e.g., feature attribution, causal diagrams), model (e.g., attention mechanisms, knowledge graphs), and output (e.g., SHAP, counterfactuals)-and summarize their objectives, trade-offs, and practical applications. We further synthes

SUBMITTER: Tang F 

PROVIDER: S-EPMC12546238 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature