mlsanityData to 14 Oct 2026

Enhancing Online Recruitment with Category-Aware MoE and LLM-based Data Augmentation

arXiv 2604.21264 ↗First posted 23 Apr 2026cs.AIcited by 0

On the map: Mixture-of-experts → MoE large language models

Abstract (arXiv)

Person-Job Fit (PJF) is a critical component for online recruitment. Existing approaches face several challenges, particularly in handling low-quality job descriptions and similar candidate-job pairs, which impair model performance. To address these challenges, this paper proposes a large language model (LLM) based method with two novel techniques: (1) LLM-based data augmentation, which polishes and rewrites low-quality job descriptions by leveraging chain-of-thought (COT) prompts, and (2) category-aware Mixture of Experts (MoE) that assists in identifying similar candidate-job pairs. This MoE module incorporates category embeddings to dynamically assign weights to the experts and learns more distinguishable patterns for similar candidate-job pairs. We perform offline evaluations and online A/B tests on our recruitment platform. Our method relatively surpasses existing methods by 2.40% in AUC and 7.46% in GAUC, and boosts click-through conversion rate (CTCVR) by 19.4% in online tests, saving millions of CNY in external headhunting expenses.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in MoE large language models, one of 12 directions in Mixture-of-experts. Builds on 11 of its 47 references that are in the map. First posted on arXiv 23 Apr 2026.

Builds on

11 of 47 references.

Closest ancestors · 2

Same direction, newest first

  1. 2020GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding30 Jun
  2. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer23 Jan

Other references · 9

  1. 2017Attention Is All You Needcited by 60,361
  2. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  3. 2018Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Ratecited by 173
  4. 2018Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approachcited by 39
  5. 2023A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analyticscited by 18
  6. 2020Learning Effective Representations for Person-Job Fit by Feature Fusioncited by 16
  7. 2021Optimizing Rankings for Recommendation in Matching Marketscited by 12
  8. 2018Talent Search and Recommendation Systems at LinkedIn: Practical Challenges and Lessons Learnedcited by 11
  9. 2022Person-job fit estimation from candidate profile and related recruitment history with co-attention neural networkscited by 3

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Enhancing Talent Search Ranking with Role-Aware Expert Mixtures and LLM-based Fine-Grained Job Descriptions
  2. 2025Enhancing Talent Employment Insights Through Feature Extraction with LLM Finetuning
  3. 2026Synapse: Evolving Job-Person Fit with Explainable Two-phase Retrieval and LLM-guided Genetic Resume Optimization
  4. 2025AI Hiring with LLMs: A Context-Aware and Explainable Multi-Agent Framework for Resume Screening
  5. 2023Generative Job Recommendations with Large Language Model
  6. 2026Evaluating LLM Behavior in Hiring: Implicit Weights, Fairness Across Groups, and Alignment with Human Preferences
  7. 2026Mira-Embeddings-V1: Domain-Adapted Semantic Reranking for Recruitment via LLM-Synthesized Data
  8. 2026An Efficient Long-Context Ranking Architecture With Calibrated LLM Distillation: Application to Person-Job Fit