| Titre : |
Multi-axis agile learning engine for matching : a framework for transparent and flexible candidate-job matching |
| Type de document : |
document électronique |
| Auteurs : |
Amine Maalem, Auteur ; Hakim Fourar Laidi, Directeur de thèse |
| Editeur : |
[S.l.] : [s.n.] |
| Année de publication : |
2025 |
| Importance : |
1 fichier PDF (2.5 Mo) |
| Présentation : |
ill. |
| Note générale : |
Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Génie Industriel.Date Science et intelligence artificiel : Alger, École Nationale Polytechnique : 2025
Bibliogr. p. 58 - 59 |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Recommender Systems
Person-Job Fit
Explainable AI (XAI)
Semantic Search
Natural
Language Processing (NLP)
Recruitment Technology
Vector Databases
Human-Computer Interaction |
| Index. décimale : |
PI00825 |
| Résumé : |
Modern recruitment faces a critical challenge: state-of-the-art semantic search engines, while powerful, operate as opaque ”black boxes” that lack the explainability and specificity required by professional recruiters. This project addresses this challenge through a dual-track approach. First, it details the development of a high-performance Industrial Baseline Engine, a system delivered to the host company that uses Google Gemini and the FAISS library to fulfill all requirements of a modern semantic matching solution.
Second, using the insights gained from the baseline’s inherent limitations, this thesis introduces an advanced research framework. This framework is named MAALEM, an acronym for Multi-Axis Agile Learning Engine for Matching. The name, which translates to ”master” or ”expert” in Arabic and is also the surname of the author, reflects the project’s core ambition: to achieve a masterful and nuanced understanding of person-job fit. The MAALEM framework’s core innovation is its deconstructed architecture, which moves beyond a single similarity score to evaluate candidates along multiple, transparent, and interpretable dimensions. This design prioritizes justified reasoning and user control, transforming the AI from an opaque filter into a genuine decision-support partner. To validate this new paradigm, a comprehensive suite of three novel benchmarks was created, including an adversarial test designed to probe model intelligence and resilience. Empirical results demonstrate that while the Industrial Baseline is a powerful tool, the MAALEM framework significantly outperforms it and all other standard models, especially under adversarial conditions. It proved uniquely effective at rejecting deceptive candidates while simultaneously identifying non-obvious ”hidden gem” profiles. MAALEM therefore represents a new vision for recruitment AI one that is not only more accurate and robust but is also fundamentally more trustworthy and strategically aligned with the needs of its expert users. |
Multi-axis agile learning engine for matching : a framework for transparent and flexible candidate-job matching [document électronique] / Amine Maalem, Auteur ; Hakim Fourar Laidi, Directeur de thèse . - [S.l.] : [s.n.], 2025 . - 1 fichier PDF (2.5 Mo) : ill. Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Génie Industriel.Date Science et intelligence artificiel : Alger, École Nationale Polytechnique : 2025
Bibliogr. p. 58 - 59 Langues : Anglais ( eng)
| Mots-clés : |
Recommender Systems
Person-Job Fit
Explainable AI (XAI)
Semantic Search
Natural
Language Processing (NLP)
Recruitment Technology
Vector Databases
Human-Computer Interaction |
| Index. décimale : |
PI00825 |
| Résumé : |
Modern recruitment faces a critical challenge: state-of-the-art semantic search engines, while powerful, operate as opaque ”black boxes” that lack the explainability and specificity required by professional recruiters. This project addresses this challenge through a dual-track approach. First, it details the development of a high-performance Industrial Baseline Engine, a system delivered to the host company that uses Google Gemini and the FAISS library to fulfill all requirements of a modern semantic matching solution.
Second, using the insights gained from the baseline’s inherent limitations, this thesis introduces an advanced research framework. This framework is named MAALEM, an acronym for Multi-Axis Agile Learning Engine for Matching. The name, which translates to ”master” or ”expert” in Arabic and is also the surname of the author, reflects the project’s core ambition: to achieve a masterful and nuanced understanding of person-job fit. The MAALEM framework’s core innovation is its deconstructed architecture, which moves beyond a single similarity score to evaluate candidates along multiple, transparent, and interpretable dimensions. This design prioritizes justified reasoning and user control, transforming the AI from an opaque filter into a genuine decision-support partner. To validate this new paradigm, a comprehensive suite of three novel benchmarks was created, including an adversarial test designed to probe model intelligence and resilience. Empirical results demonstrate that while the Industrial Baseline is a powerful tool, the MAALEM framework significantly outperforms it and all other standard models, especially under adversarial conditions. It proved uniquely effective at rejecting deceptive candidates while simultaneously identifying non-obvious ”hidden gem” profiles. MAALEM therefore represents a new vision for recruitment AI one that is not only more accurate and robust but is also fundamentally more trustworthy and strategically aligned with the needs of its expert users. |
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