Comparative analysis of different trust metrics of user-user trust-based recommendation system

Abstract

Information overload is the biggest challenge nowadays for any website – especially e-commerce websites. However, this challenge has arisen due to the fast growth of information on the web (WWW) along with easier access to the internet. A collaborative filtering-based recommender system is the most useful application for solving the information overload problem by filtering relevant information for users according to their interests. However, the current system faces some significant limitations such as data sparsity, low accuracy, cold-start, and malicious attacks. To alleviate the above-mentioned issues, the relationship of trust incorporates in the system where it can be among users or items, such a system is known as a trust-based recommender system (TBRS). From the user perspective, the motive of a TBRS is to utilize the reliability among 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 24 trust metrics in terms of the methodology, trust properties & measurements, validation approaches, and the experimented data set.