Comparative Analysis of Different Trust Metrics of User-User Trust-Based Recommendation System

Information overload is the biggest challenge nowadays for any website, especially e-commerce websites. However, this challenge arises for the fast growth of information on the web (WWW) with easy access to the internet. Collaborative filtering based recommender system is the most useful application to solve the information overload problem by filtering relevant information for the users according to their interests. But, the existing system faces some significant limitations such as data sparsity, low accuracy, cold-start, and malicious attacks. To alleviate the mentioned issues, the relationship of trust incorporates in the system where it can be between the users or items, and such system is known as the trust-based recommender system (TBRS). From the user perspective, the motive of the TBRS is to utilize the reliability between the users to generate more accurate and trusted recommendations. However, the study aims to present a comparative analysis of different trust metrics in the context of the type of trust definition of TBRS. Also, the study accomplishes twenty-four trust metrics in terms of the methodology, trust properties \& measurement, validation approaches, and the experimented dataset.