MHSNet:An MoE-based Hierarchical Semantic Representation Network for Accurate Duplicate Resume Detection with Large Language Model

TL;DR

MHSNet is a hierarchical semantic network leveraging MoE and large language models to accurately detect duplicate resumes, addressing challenges of semantic complexity and incomplete data in talent pool management.

Contribution

It introduces a multi-level identity verification framework with MoE-enhanced representations and state-aware mechanisms for improved resume duplication detection.

Findings

Effective in handling incomplete and heterogeneous resumes

Improves accuracy of duplicate detection

Demonstrates superior performance over existing methods

Abstract

To maintain the company's talent pool, recruiters need to continuously search for resumes from third-party websites (e.g., LinkedIn, Indeed). However, fetched resumes are often incomplete and inaccurate. To improve the quality of third-party resumes and enrich the company's talent pool, it is essential to conduct duplication detection between the fetched resumes and those already in the company's talent pool. Such duplication detection is challenging due to the semantic complexity, structural heterogeneity, and information incompleteness of resume texts. To this end, we propose MHSNet, an multi-level identity verification framework that fine-tunes BGE-M3 using contrastive learning. With the fine-tuned , Mixture-of-Experts (MoE) generates multi-level sparse and dense representations for resumes, enabling the computation of corresponding multi-level semantic similarities. Moreover, the state-aware Mixture-of-Experts (MoE) is employed in MHSNet to handle diverse incomplete resumes. Experimental results verify the effectiveness of MHSNet