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

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 461-487, 2025, DOI:10.32604/cmes.2025.063973 - 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 More >

Sony Peng¹, Sophort Siet¹, Ilkhomjon Sadriddinov¹, Dae-Young Kim^2,*, Kyuwon Park^3,*, Doo-Soon Park²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2041-2057, 2025, DOI:10.32604/cmc.2025.061166 - 16 April 2025

Abstract Recommendation systems (RSs) are crucial in personalizing user experiences in digital environments by suggesting relevant content or items. Collaborative filtering (CF) is a widely used personalization technique that leverages user-item interactions to generate recommendations. However, it struggles with challenges like the cold-start problem, scalability issues, and data sparsity. To address these limitations, we develop a Graph Convolutional Networks (GCNs) model that captures the complex network of interactions between users and items, identifying subtle patterns that traditional methods may overlook. We integrate this GCNs model into a federated learning (FL) framework, enabling the model to learn… More >

S. Sridhar^1,*, D. Dhanasekaran², G. Charlyn Pushpa Latha³

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3241-3257, 2023, DOI:10.32604/iasc.2023.030361 - 17 August 2022

Abstract The content-based filtering technique has been used effectively in a variety of Recommender Systems (RS). The user explicitly or implicitly provides data in the Content-Based Recommender System. The system collects this data and creates a profile for all the users, and the recommendation is generated by the user profile. The recommendation generated via content-based filtering is provided by observing just a single user’s profile. The primary objective of this RS is to recommend a list of movies based on the user’s preferences. A content-based movie recommendation model is proposed in this research, which recommends movies… More >

Wei Fang^1,2,*, Yu Sha¹, Meihan Qi¹, Victor S. Sheng³

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 609-622, 2022, DOI:10.32604/iasc.2022.027067 - 15 April 2022

Abstract With the rapid development of personalized services, major websites have launched a recommendation module in recent years. This module will recommend information you are interested in based on your viewing history and other information, thereby improving the economic benefits of the website and increasing the number of users. This paper has introduced content-based recommendation algorithm, K-Nearest Neighbor (KNN)-based collaborative filtering (CF) algorithm and singular value decomposition-based (SVD) collaborative filtering algorithm. However, the mentioned recommendation algorithms all recommend for a certain aspect, and do not realize the recommendation of specific movies input by specific users which… More >