Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Venkata Seshu Kiran, T.; Shaik, Mohammed Ali Title A name given to the resource Optimizing Interpretability in Recommender Systems using a Hybrid Model based on Matrix Factorization and Neural Networks Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 4th International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2025;pp.239-244 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICAAIC64647.2025.11330386" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICAAIC64647.2025.11330386</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034854696?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034854696?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Venkata Seshu Kiran T., School of Computer Science &amp; Artificial Intelligence, Sr University, Telangana, Warangal, 506371, India, Malla Reddy Technical Campus (A Constituent Unit of Malla Reddy Vishwavidyapeeth, Deemed to be University, Hyderabad), Department of Cse, Telangana, India; Shaik M.A., School of Computer Science &amp; Artificial Intelligence, Sr University, Telangana, Warangal, 506371, India Description An account of the resource Recommender systems play a crucial role in the direction of user choices in e-commerce, media, and online services, clearly, there is a trade-off between predictive accuracy and interpretability. In this paper, a new hybrid model that combines Matrix Factorization and a Neural Network framework to maximize the performance of recommendation as well as explainability has been suggested. The model uses Latent factor representation of Matrix Factorization to provide the global user item interactions, and the Neural Network component finds nonlinear interaction and contextual patterns in the data. The hybrid architecture is trained and tested on a Kaggle dataset of 100,000 user-item interactions with several numerical and categorical characteristics. It compares to standalone methods in that the system is more superior with an accuracy of 94.5, F1-score of 0.945, mean absolute error (MAE) of 0.087 and root mean squared error (RMSE) of 0.112. It is proven by computational analysis to have efficient training convergence and low inference latency, allowing real-time recommendations on Google Colab. The proposed solution bridges the gap between performance and transparency since it can be applied and is credible by being predictive and understandable at the same time. The study has implications in intelligent, explainable and scalable recommenders systems in diverse areas of application. 2025 IEEE. Subject The topic of the resource Deep Learning; Hybrid Model; Interpretability; Matrix Factorization; Neural Networks; Recommender Systems Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833156587-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online