ItemConference Paper

Optimizing Interpretability in Recommender Systems using a Hybrid Model based on Matrix Factorization and Neural Networks

Title
Optimizing Interpretability in Recommender Systems using a Hybrid Model based on Matrix Factorization and Neural Networks
Creator
Venkata Seshu Kiran, T.; Shaik, Mohammed Ali
Description
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.
Source
4th International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2025;pp.239-244
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Deep Learning; Hybrid Model; Interpretability; Matrix Factorization; Neural Networks; Recommender Systems
Coverage
Venkata Seshu Kiran T., School of Computer Science & 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 & Artificial Intelligence, Sr University, Telangana, Warangal, 506371, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833156587-9;
Format
online
Language
English
Type
Conference paper

Citation

Venkata Seshu Kiran, T.; Shaik, Mohammed Ali, “Optimizing Interpretability in Recommender Systems using a Hybrid Model based on Matrix Factorization and Neural Networks,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25887.

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