Evolutionary Deep Learning Based CNNTWSVM Movie Recommendation System
Evolutionary Deep Learning Based CNNTWSVM Movie Recommendation System |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-9 |
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| Year of Publication : 2025 | ||
| Author : Nisha Bhalse, Ramesh Thakur, Archana Thakur | ||
| DOI : 10.14445/22315381/IJETT-V73I9P120 | ||
How to Cite?
Nisha Bhalse, Ramesh Thakur, Archana Thakur,"Evolutionary Deep Learning Based CNNTWSVM Movie Recommendation System", International Journal of Engineering Trends and Technology, vol. 73, no. 9, pp.217-228, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I9P120
Abstract
In this age of digital content, a movie recommendation system is essential for users to recommend highly accurate and efficient personalized movies and services. Over the past decade, researchers have been studying recommendation algorithms, and they have been implemented on various platforms, including e-commerce and movie streaming services. However, traditional recommendation algorithms suffer from cold-start and sparsity problems. The proposed CNNTWSVM movie recommendation system, which integrates the Convolutional Neural Networks (CNN) with Twin Support Vector Machines (TWSVM), was presented in this paper. The experimental performance evaluation of the CNNTWSVM personalized movie recommendation system against traditional algorithms, including Matrix Factorization, Autoencoder, Neural Collaborative Filtering, and standard CNN models, using the 1M MovieLens dataset. The results show the proposed CNNTWSVM model outperforms, achieving the lowest root mean squared and mean absolute errors of 0.805 and 0.63. The proposed CNNTWSVM model minimizes prediction errors effectively. It achieves the highest values of accuracy (95.2%), precision (97.5%), recall (89.0%), normalized discounted cumulative gain (89.0%), diversity (94.7%), and serendipity (93.2%). The proposed CNNTWSVM personalized movie recommendations system recommends accurate and relevant movies and also exposes users to a diverse selection of lesser-known yet engaging movies according to their preferences.
Keywords
Collaborative filtering, Twin support vector machines, Convolutional Neural Networks, Recommendation system.
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