With increasing developments in artificial intelligence and the need for more personalized digital experiences, user trust and engagement have become relevant factors to be considered for the success of e-commerce recommender systems. This study presents a bibliometric analysis of research trends from 2003 to 2023 by exploring the evolution of trust and engagement in this domain. Using data from the Scopus database, we investigated publication trends, influential works, key contributors, and emerging research themes. Our results reveal a surge in research output between 2020 and 2023, which shows an increasing scholarly appreciation of trust as a critical determinant of user engagement of recommender systems. The leading role of China in global contributions emphasized its reliance on social commerce models, where recommendations are powered by a community-based trust mechanism to drive user engagement. While foundational topics such as collaborative filtering and machine learning remain central, emerging themes (explainability, blockchain integration, and adaptive AI) highlight a shift toward more user-centric and secure systems. These reinforce trust through transparency and security while boosting engagement through active personalization. Thematic evolution from algorithmic development to AI-driven innovations shows how transparency, personalization, and security serve as vital trust-building influencers that drive user engagement in recommender systems. Also, regional disparities in research output, especially in Africa and South America reveal considerable gaps in understanding culturally specific trust factors and engagement patterns. This indicates the need for collaborative studies to develop inclusive recommender systems tailored to local context to bridge these gaps. These findings reflect that trust and engagement are not simply complementary features, but fundamental pillars that are influencing the future of e-commerce recommender systems. As AI advances toward explainable, secure, and adaptive designs, this research calls for urgent globally inclusive frameworks that address both technological sophistication and cultural diversity to ensure that recommender systems emerge as equitable tools for global e-commerce.

In our present environment, heart diseases are very rampart and they describe the various types of diseases that affect the heart. They account for the leading cause of death word-wide especially, in Africa. It is therefore very important for individuals to have adequate knowledge about their heart health in order to avoid the risk of decreased life expectancy. The high mortality rate of heart (cardiovascular) diseases is attributed to the unequal ratio of patients to scarcity of medical experts who can provide medical care, also patients are not always warn to waiting long hours on queue in the hospital, especially in cases of emergency. This paper designed and implemented a Mobile Neuro-fuzzy System that uses the combination of the intelligence technique of Artificial Neural Networks (ANN) and the human-like reasoning style of Fuzzy Logic to diagnose and suggest possible treatments for cardiovascular diseases through interactivity with user. It employs programs like MySQL, PHP, JAVA (Android) and XML (Android Studio) while tools like XAMPP, PhpStorm and Android O/S were used to integrate these techniques together. The system, proved to be of enormous advantage in diagnosing heart diseases, as it diagnoses and learns about each user per time, to provide adequate and appropriate results and also makes reliable predictions to users.