Semantic Candidate-Job Matching: A Comparative Evaluation of Dense Embedding Models in Hybrid Retrieval

arXiv:2609.23307 · cs.IR, cs.AI · Submitted 2026-09-20 · Read on arXiv

cs.IR, cs.AI

Submitted: 2026-09-20

Updated: 2026-09-20

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Fine-Tuning — 58× in this paper · explained in 5 episodes
• Benchmark — 16× in this paper · explained in 2 episodes
• Transformer — 14× in this paper · explained in 2 episodes
• Adaptation — 13× in this paper · explained in 1 episode
• LLM-as-a-Judge — 13× in this paper · explained in 3 episodes
• Embeddings — 10× in this paper · explained in 2 episodes
• Reciprocal Rank Fusion — 8× in this paper
• Calibration — 7× in this paper · explained in 7 episodes
• Domain Adaptation — 7× in this paper · explained in 5 episodes
• Cosine Similarity — 6× in this paper · explained in 3 episodes
• AUC — 5× in this paper · explained in 2 episodes
• Mean Reciprocal Rank — 4× in this paper
• Accuracy — 3× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Cached Multiple Negatives Ranking Loss — 3× in this paper
• Consistency — 2× in this paper · explained in 3 episodes
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Domain Shift — 2× in this paper · explained in 4 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Accent Preservation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• Hybrid System — 1× in this paper · explained in 2 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes

Sources

• Degree Associated Edge Reconstruction Parameters of Strong Double Brooms
• AnglE-optimized Text Embeddings
• MTEB: Massive Text Embedding Benchmark
• Passage Re-ranking with BERT
• Joint Pre-training and Local Re-training: Transferable Representation Learning on Multi-source Knowledge Graphs

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