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 >

Yong Zheng^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4315-4328, 2025, DOI:10.32604/cmc.2025.063498 - 19 May 2025

Abstract A recommender system is a tool designed to suggest relevant items to users based on their preferences and behaviors. Collaborative filtering, a popular technique within recommender systems, predicts user interests by analyzing patterns in interactions and similarities between users, leveraging past behavior data to make personalized recommendations. Despite its popularity, collaborative filtering faces notable challenges, and one of them is the issue of grey-sheep users who have unusual tastes in the system. Surprisingly, existing research has not extensively explored outlier detection techniques to address the grey-sheep problem. To fill this research gap, this study conducts… More >

Weitao Ha¹, Sheng Gang², Yahya D. Navaei³, Abubakar S. Gezawa⁴, Yaser A. Nanehkaran^2,5,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3025-3057, 2025, DOI:10.32604/cmc.2025.061343 - 16 April 2025

Abstract Music recommendation systems are essential due to the vast amount of music available on streaming platforms, which can overwhelm users trying to find new tracks that match their preferences. These systems analyze users’ emotional responses, listening habits, and personal preferences to provide personalized suggestions. A significant challenge they face is the “cold start” problem, where new users have no past interactions to guide recommendations. To improve user experience, these systems aim to effectively recommend music even to such users by considering their listening behavior and music popularity. This paper introduces a novel music recommendation system… More >

Weiming Huang^1,2, Baisong Liu^1,*, Zhaoliang Wang¹

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4449-4469, 2024, DOI:10.32604/cmc.2024.050389 - 20 June 2024

Abstract In the tag recommendation task on academic platforms, existing methods disregard users’ customized preferences in favor of extracting tags based just on the content of the articles. Besides, it uses co-occurrence techniques and tries to combine nodes’ textual content for modelling. They still do not, however, directly simulate many interactions in network learning. In order to address these issues, we present a novel system that more thoroughly integrates user preferences and citation networks into article labelling recommendations. Specifically, we first employ path similarity to quantify the degree of similarity between user labelling preferences and articles… More >

Awais Ahmad¹, Sohail Jabbar^1,*, Sheeraz Akram¹, Anand Paul², Umar Raza³, Nuha Mohammed Alshuqayran¹

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3129-3150, 2024, DOI:10.32604/cmc.2024.045385 - 26 March 2024

Abstract This paper presents an innovative approach to enhance the querying capability of ChatGPT, a conversational artificial intelligence model, by incorporating voice-based interaction and a convolutional neural network (CNN)-based impaired vision detection model. The proposed system aims to improve user experience and accessibility by allowing users to interact with ChatGPT using voice commands. Additionally, a CNN-based model is employed to detect impairments in user vision, enabling the system to adapt its responses and provide appropriate assistance. This research tackles head-on the challenges of user experience and inclusivity in artificial intelligence (AI). It underscores our commitment to… More >

S. Abinaya^*, K. Uttej Kumar

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2269-2286, 2024, DOI:10.32604/cmc.2024.047167 - 27 February 2024

Abstract A Recommender System (RS) is a crucial part of several firms, particularly those involved in e-commerce. In conventional RS, a user may only offer a single rating for an item-that is insufficient to perceive consumer preferences. Nowadays, businesses in industries like e-learning and tourism enable customers to rate a product using a variety of factors to comprehend customers’ preferences. On the other hand, the collaborative filtering (CF) algorithm utilizing AutoEncoder (AE) is seen to be effective in identifying user-interested items. However, the cost of these computations increases nonlinearly as the number of items and users… More >

Jiayi Ma, Tianhao Sun^*, Xiaodong Zhang

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3569-3584, 2023, DOI:10.32604/cmc.2023.040005 - 08 October 2023

Abstract Sequential recommendation based on a multi-interest framework aims to analyze different aspects of interest based on historical interactions and generate predictions of a user’s potential interest in a list of items. Most existing methods only focus on what are the multiple interests behind interactions but neglect the evolution of user interests over time. To explore the impact of temporal dynamics on interest extraction, this paper explicitly models the timestamp with a multi-interest network and proposes a time-highlighted network to learn user preferences, which considers not only the interests at different moments but also the possible… More >

R. Sujatha^1,*, T. Abirami²

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1775-1787, 2023, DOI:10.32604/csse.2023.031552 - 09 February 2023

Abstract The digital technologies that run based on users’ content provide a platform for users to help air their opinions on various aspects of a particular subject or product. The recommendation agents play a crucial role in personalizing the needs of individual users. Therefore, it is essential to improve the user experience. The recommender system focuses on recommending a set of items to a user to help the decision-making process and is prevalent across e-commerce and media websites. In Context-Aware Recommender Systems (CARS), several influential and contextual variables are identified to provide an effective recommendation. A… More >

Sharmilee Kannan^1,*, R. U. Anitha², M. Divayapushpalakshmi³, K. S. Kalaivani⁴

Computer Systems Science and Engineering, Vol.45, No.2, pp. 2001-2016, 2023, DOI:10.32604/csse.2023.031965 - 03 November 2022

Abstract The rising use of mobile technology and smart gadgets in the field of health has had a significant impact on the global community. Health professionals are increasingly making use of the benefits of these technologies, resulting in a major improvement in health care both in and out of clinical settings. The Internet of Things (IoT) is a new internet revolution that is a rising research area, particularly in health care. Healthcare Monitoring Systems (HMS) have progressed rapidly as the usage of Wearable Sensors (WS) and smartphones have increased. The existing framework of conventional telemedicine’s store-and-forward method… More >

P. N. Ramesh^1,*, S. Kannimuthu²

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3845-3860, 2023, DOI:10.32604/iasc.2023.031329 - 17 August 2022

Abstract The use of programming online judges (POJs) has risen dramatically in recent years, owing to the fact that the auto-evaluation of codes during practice motivates students to learn programming. Since POJs have greater number of programming problems in their repository, learners experience information overload. Recommender systems are a common solution to information overload. Current recommender systems used in e-learning platforms are inadequate for POJ since recommendations should consider learners’ current context, like learning goals and current skill level (topic knowledge and difficulty level). To overcome the issue, we propose a context-aware practice problem recommender system… More >