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Empirical Research on the Impact of Personalized Recommendation Diversity

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Title:Empirical Research on the Impact of Personalized Recommendation Diversity
Authors:Zhang, Lin
Yan, Qiang
Lu, Junqiang
Chen, Yongqiang
Liu, Yi
Keywords:Decision Support for Smart Cities
Decision Analytics, Mobile Services, and Service Science
Personalized recommendation, Product ratings, Product sales, Long tail, Diversity
Date Issued:08 Jan 2019
Abstract:Personalized recommendation has important implications in raising online shopping efficiency and increasing product sales. There has been wide interest in finding ways to provide more efficient personalized recommendations. Most existing studies focus on how to improve the accuracy of the recommendation algorithms, or are more concerned on ways to increase consumer satisfaction. Unlike these studies, our study focuses on the process of decision-making, using long tail theory as a basis, to reveal the mechanisms involved in consumers’ adoption of recommendations. This paper analyzes the effect of personalized recommendations from two angles: product sales and ratings, and tries to point out differences in consumer preferences between mainstream products and niche products, high rating products and low rating products, search products and experience products. The study verifies that consumers demand diversity in the recommended content, and also provides suggestions on how to better plan and operate a personalized recommendation system.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/59571
ISBN:978-0-9981331-2-6
DOI:10.24251/HICSS.2019.159
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Decision Support for Smart Cities


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