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arXiv AI By Syed Ali Ahmed (National University of Computer and Emerging Sciences, Karachi, Pakistan), Malaika Raza (National University of Computer and Emerging Sciences, Karachi, Pakistan), Muhammad Shoaib Siddiqui (Islamic University of Madinah, Madinah, Saudi Arabia), Muhammad Rafi (National University of Computer and Emerging Sciences, Karachi, Pakistan)

Beyond Accuracy: Centroid-Guided Contrastive Loss for Structured Fraudulent Job Posting Detection

Read the original on arXiv AI →

The paper introduces Centroid‑Guided Contrastive Loss (CGCL), a novel loss function that combines classification with dense clustering to improve fraudulent job posting detection. CGCL reshapes the latent space using a centroid‑driven top‑k push‑and‑pull mechanism, ensuring accurate decision boundaries while preserving compact clustering of similar fake posts. Experiments on the EMSCAD benchmark show that CGCL achieves state‑of‑the‑art performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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