| Titre : | Interpretable recommender systems : a hybrid architecture with logical and collaborative filtering layers |
| Auteurs : | Nadhir Mazari Boufares, Auteur ; Samia Beldjoudi, Directeur de thèse |
| Type de document : | document électronique |
| Editeur : | [S.l.] : [s.n.], 2024 |
| Format : | 1 fichier PDF (10 Mo) / ill. |
| Note générale : |
Mode d'accès : accès au texte intégral par intranet
Mémoire de Projet de Fin d’Etudes : Génie Industriel. Data Science-Intelligence Artificielle : Alger, Ecole Nationale Polytechnique : 2024 Bibliogr. p. 70 - 72 |
| Langues : | Anglais |
| Index. décimale : | PI02524 |
| Tags : | Recommendation system Reasoning Interpretability |
| Résumé : | Recommender systems (RSs) are rapidly evolving with increasing personalization to meet new constraints and improve performance on digital platforms. However, a significant issue remains: the lack of transparency in their decision-making, particularly with black-box approaches. Integrating logical reasoning and symbolic methods offers a promising solution for enhancing interpretability, but these methods are often underutilized. This thesis proposes a novel RS model that enhances interpretability for end users. Our architecture integrates a logical layer for generating rules from user and item attributes, alongside a graph convolutional network for collaborative filtering. By combining these components, our model generates recommendation scores with improved transparency and interpretability. |
Exemplaires (1)
| Cote | Support | Localisation | Section | Disponibilité | Spécialité | Etat_Exemplaire |
|---|---|---|---|---|---|---|
| PI02524 | Ressources électroniques | Bibliothèque centrale | Projet Fin d'Etudes | Disponible | Data sciences_Intelligence artificielle | Téléchargeable |
Documents numériques (1)
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MAZARI-BOUFARES.Nadrih.pdf URL
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