An opinion-based decision model for recommender systems

Abstract

Purpose - A good recommender system helps users find items of interest on the web and can provide recommendations based on user preferences. In contrast to automatic technology-generated recommender systems, this paper aims to use dynamic expert groups that are automatically formed to recommend domain-specific documents for general users. In addition, it aims to test several effectiveness measures of rank order to determine if the top-ranked lists recommended by the experts were reliable. Design/methodology/approach - In the approach, expert groups evaluate web documents to provide a recommender system for general users. The authority and make-up of the expert group are adjusted through user feedback. The system also uses various measures to gauge the difference between the opinions of experts and those of general users to improve the evaluation effectiveness. Findings - The proposed system is efficient when there is major support from experts and general users. The recommender system is especially effective where there is a limited amount of evaluation data from general users. Originality/value - This is an original study of how to effectively recommend web documents to users based on the opinions of human experts. Simulation results were provided to show the effectiveness of the dynamic expert group for recommender systems.