| _version_ | 1866902181112709120 |
|---|---|
| author | Dr. Deepjyoti Roy, Prof. Dr. Rohini Pundalik Onkare, Muthumari, Amit S. Tiwari, Shah Rakesh Jagdishchandra, Amartya Ghosh, Dr. Nagendra Nath Giri |
| author_facet | Dr. Deepjyoti Roy, Prof. Dr. Rohini Pundalik Onkare, Muthumari, Amit S. Tiwari, Shah Rakesh Jagdishchandra, Amartya Ghosh, Dr. Nagendra Nath Giri |
| contents | <p class="MsoBodyText">Personalized<span> </span>recommender<span> </span>systems<span> </span>play<span> </span>a<span> </span>central<span> </span>role<span> </span>in<span> </span>modern<span> </span>digital<span> </span>platforms,<span> </span>yet<span> </span>many<span> </span>high-performing models<span> </span>operate<span> </span>as<span> </span>opaque<span> </span>systems,<span> </span>limiting<span> </span>user<span> </span>trust<span> </span>and<span> </span>practical<span> </span>adoption.<span> </span>Explainable<span> </span>artificial<span> </span>intelligence has<span> </span>emerged<span> </span>as<span> </span>a<span> </span>promising<span> </span>approach<span> </span>to<span> </span>address<span> </span>this<span> </span>challenge<span> </span>by<span> </span>enhancing<span> </span>transparency while<span> </span>maintaining predictive capability. This study investigates the impact of integrating explainable features into a deep learning-based personalized recommender system to improve both performance and interpretability. Using the<span> </span>REASONER<span> </span>dataset,<span> </span>which<span> </span>includes<span> </span>user-item<span> </span>interactions<span> </span>enriched<span> </span>with<span> </span>user<span> </span>attributes,<span> </span>personality<span> </span>traits, and multi-aspect tags, a logistic regression model was implemented as a baseline and compared with a feedforward<span> </span>neural<span> </span>network.<span> </span>Model<span> </span>performance<span> </span>was<span> </span>evaluated<span> </span>using<span> </span>accuracy,<span> </span>precision,<span> </span>recall,<span> </span>F1<span> </span>score,<span> </span>and ROC-AUC,<span> </span>with<span> </span>threshold<span> </span>tuning<span> </span>and<span> </span>feature<span> </span>importance<span> </span>analysis<span> </span>applied<span> </span>to<span> </span>optimize<span> </span>and<span> </span>interpret<span> </span>the<span> </span>neural network.<span> </span>The<span> </span>results<span> </span>show<span> </span>that<span> </span>the<span> </span>neural<span> </span>network<span> </span>achieved<span> </span>superior<span> </span>discriminative<span> </span>performance<span> </span>with<span> </span>a<span> </span>ROC-AUC<span> </span>of<span> </span>0.729<span> </span>and<span> </span>improved<span> </span>minority-class<span> </span>recall<span> </span>after<span> </span>optimization.<span> </span>Feature<span> </span>importance<span> </span>analysis<span> </span>revealed<span> </span>that interest tags, video tags, and reason-based attributes were the most influential predictors, whereas demographic variables contributed less significantly. These findings indicate that incorporating explainable, behavior-driven features enhances both the effectiveness and transparency of recommendation models. Overall, the study highlights the importance of combining deep learning with explainable inputs to develop more reliable, user-centered recommender systems.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20078428 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR PERSONALIZED RECOMMENDER SYSTEMS USING DEEP LEARNING MODEL Dr. Deepjyoti Roy, Prof. Dr. Rohini Pundalik Onkare, Muthumari, Amit S. Tiwari, Shah Rakesh Jagdishchandra, Amartya Ghosh, Dr. Nagendra Nath Giri explainable artificial intelligence, recommender systems, deep learning, personalization, transparency <p class="MsoBodyText">Personalized<span> </span>recommender<span> </span>systems<span> </span>play<span> </span>a<span> </span>central<span> </span>role<span> </span>in<span> </span>modern<span> </span>digital<span> </span>platforms,<span> </span>yet<span> </span>many<span> </span>high-performing models<span> </span>operate<span> </span>as<span> </span>opaque<span> </span>systems,<span> </span>limiting<span> </span>user<span> </span>trust<span> </span>and<span> </span>practical<span> </span>adoption.<span> </span>Explainable<span> </span>artificial<span> </span>intelligence has<span> </span>emerged<span> </span>as<span> </span>a<span> </span>promising<span> </span>approach<span> </span>to<span> </span>address<span> </span>this<span> </span>challenge<span> </span>by<span> </span>enhancing<span> </span>transparency while<span> </span>maintaining predictive capability. This study investigates the impact of integrating explainable features into a deep learning-based personalized recommender system to improve both performance and interpretability. Using the<span> </span>REASONER<span> </span>dataset,<span> </span>which<span> </span>includes<span> </span>user-item<span> </span>interactions<span> </span>enriched<span> </span>with<span> </span>user<span> </span>attributes,<span> </span>personality<span> </span>traits, and multi-aspect tags, a logistic regression model was implemented as a baseline and compared with a feedforward<span> </span>neural<span> </span>network.<span> </span>Model<span> </span>performance<span> </span>was<span> </span>evaluated<span> </span>using<span> </span>accuracy,<span> </span>precision,<span> </span>recall,<span> </span>F1<span> </span>score,<span> </span>and ROC-AUC,<span> </span>with<span> </span>threshold<span> </span>tuning<span> </span>and<span> </span>feature<span> </span>importance<span> </span>analysis<span> </span>applied<span> </span>to<span> </span>optimize<span> </span>and<span> </span>interpret<span> </span>the<span> </span>neural network.<span> </span>The<span> </span>results<span> </span>show<span> </span>that<span> </span>the<span> </span>neural<span> </span>network<span> </span>achieved<span> </span>superior<span> </span>discriminative<span> </span>performance<span> </span>with<span> </span>a<span> </span>ROC-AUC<span> </span>of<span> </span>0.729<span> </span>and<span> </span>improved<span> </span>minority-class<span> </span>recall<span> </span>after<span> </span>optimization.<span> </span>Feature<span> </span>importance<span> </span>analysis<span> </span>revealed<span> </span>that interest tags, video tags, and reason-based attributes were the most influential predictors, whereas demographic variables contributed less significantly. These findings indicate that incorporating explainable, behavior-driven features enhances both the effectiveness and transparency of recommendation models. Overall, the study highlights the importance of combining deep learning with explainable inputs to develop more reliable, user-centered recommender systems.</p> |
| title | EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR PERSONALIZED RECOMMENDER SYSTEMS USING DEEP LEARNING MODEL |
| topic | explainable artificial intelligence, recommender systems, deep learning, personalization, transparency |
| url | https://doi.org/10.5281/zenodo.20078428 |