Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Comparative Analysis of Different Trust Metrics in User-User Trust-Based Recommender System

Version 1 : Received: 15 November 2020 / Approved: 18 November 2020 / Online: 18 November 2020 (10:50:52 CET)

How to cite: Roy, F. A Comparative Analysis of Different Trust Metrics in User-User Trust-Based Recommender System. Preprints 2020, 2020110466 (doi: 10.20944/preprints202011.0466.v1). Roy, F. A Comparative Analysis of Different Trust Metrics in User-User Trust-Based Recommender System. Preprints 2020, 2020110466 (doi: 10.20944/preprints202011.0466.v1).

Abstract

Information overload is the biggest challenges nowadays for any website, especially the e-commerce website. 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 like 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 aim of the paper is to present a comparative analysis of different trust metrics in the context of the type of trust definition of TBRS. Also, the study accomplishes on twenty-four trust metrics in terms of the methodology, trust properties & measurement, validation approaches, and the experimented dataset.

Subject Areas

trust-based recommender system; pearson correlation coefficient; confidence; mean absolute error; precision; recall; coverage

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