Title: Enhance personalised recommendations by exploring online social relations

Authors: Xiaoyun He; Chuleeporn Changchit

Addresses: Department of Information Systems, Auburn University at Montgomery, Montgomery, AL 36117, USA ' Department of Decision Sciences and Economics, Texas A&M University – Corpus Christi, Corpus Christi, TX 78412, USA

Abstract: Personalised digital recommendations are widely used to improve customer experience and drive sales. Although recent research suggests that online social relations influence users' both product choices and ratings, few studies have examined them in the context of personalised recommendations. In this study, we aim to explore how online social relations can be leveraged to enhance personalised recommendations. The empirical results demonstrate that incorporating the ratings from a user's social circle improves accuracy and coverage of personalised recommendations; In addition, differentiating these social ratings helps increase the recommendation diversity while limiting the loss of accuracy. The findings have important implications for the applicability of recommender systems in modern online business and social environment.

Keywords: social relations; recommendation accuracy; the diversity; online recommendation; personalised recommendation; online relations.

DOI: 10.1504/IJADS.2024.138189

International Journal of Applied Decision Sciences, 2024 Vol.17 No.3, pp.341 - 360

Received: 26 May 2022
Accepted: 18 Jan 2023
Published online: 30 Apr 2024 *

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