Open Access

ARTICLE

A Deep Collaborative Neural Generative Embedding for Rating Prediction in Movie Recommendation Systems

Ravi Nahta¹, Nagaraj Naik^2,*, Srivinay³, Swetha Parvatha Reddy Chandrasekhara⁴

1 Department of Computer Science and Engineering, Indian Institute of Information Technology Vadodara, Gandhinagar City, 382028, India
2 Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
3 Department of Information Science and Engineering, B.M.S. College of Engineering, Bengaluru City, 560019, India
4 Department of Computer Science and Engineering, B.M.S. College of Engineering, Bengaluru City, 560 019, India

* Corresponding Author: Nagaraj Naik. Email:

Computer Modeling in Engineering & Sciences 2025, 144(1), 461-487. https://doi.org/10.32604/cmes.2025.063973

Received 31 January 2025; Accepted 26 June 2025; Issue published 31 July 2025

Abstract

The exponential growth of over-the-top (OTT) entertainment has fueled a surge in content consumption across diverse formats, especially in regional Indian languages. With the Indian film industry producing over 1500 films annually in more than 20 languages, personalized recommendations are essential to highlight relevant content. To overcome the limitations of traditional recommender systems—such as static latent vectors, poor handling of cold-start scenarios, and the absence of uncertainty modeling—we propose a deep Collaborative Neural Generative Embedding (C-NGE) model. C-NGE dynamically learns user and item representations by integrating rating information and metadata features in a unified neural framework. It uses metadata as sampled noise and applies the reparameterization trick to capture latent patterns better and support predictions for new users or items without retraining. We evaluate C-NGE on the Indian Regional Movies (IRM) dataset, along with MovieLens 100 K and 1 M. Results show that our model consistently outperforms several existing methods, and its extensibility allows for incorporating additional signals like user reviews and multimodal data to enhance recommendation quality.

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Keywords

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