Citation

Muthusamy, Maragatharajan and Sureshkumar, Aanjankumar and Dhanaraj, Rajesh Kumar and Sayeed, Md Shohel and Alkhayyat, Ahmad and Sivakumar, Nithya Rekha and Palani, Karthik (2026) Gated Recurrent Unit-Based Deep Learning Framework for Personalized and Sequential Smart TV Content Recommendation. International Journal of Computational Intelligence Systems, 19 (1). ISSN 1875-6883

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Abstract

With the rapid growth of smart TVs, the development of efficient recommendation systems has become essential to enhance the user experience. Most existing recommendation systems rely on collaborative filtering and matrix factorization techniques, which are limited in capturing temporal dependencies and sequential user behaviour. To overcome these issues, this research uses Gated Recurrent Units with an attention mechanism for smart TV content, leveraging sequential data to understand temporal patterns in user viewing sequences. This utilizes cross dataset validation on movielens100k and 1 M dataset for ensuring scalability. The proposed recommender statistical significance pipeline is designed with Item embeddings, stacked GRU layers, and a lightweight attention mechanism to minimize computational overhead on resource-constrained edge devices. Model performance has been evaluated using fivefold cross-validation and computational Flops with identical architectures. The proposed model has produced a normalized Root Mean Square Error (RMSE) of 0.25 with MAE validation confirmed using a paired t-test at a 0.05 significance level, demonstrating stable performance across different datasets. In contrast, the graph-based model with an autoencoder has 0.8, the Deep Belief network algorithm with Monarch Butterfly Optimisation has 0.9, and KNN and the restricted Boltzmann machine learning algorithm have 0.8. The proposed model shows significant improvement, achieving normalized RMSE and better generalization across k-fold validations for real time recommendation scenarios.

Item Type:	Article
Uncontrolled Keywords:	TV program recommendation, gated recurrent unit, statistical test
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	03 Apr 2026 02:57
Last Modified:	03 Apr 2026 02:57
URII:	http://shdl.mmu.edu.my/id/eprint/15688

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