摘要:

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. © 2019 IEEE Computer Society. All rights reserved.

附注:

Conference code: 169517Cited By :1Export Date: 9 November 2022Funding details: National Natural Science Foundation of China, NSFC, 71673158, 71804083, 91646101Funding details: National Key Research and Development Program of China, NKRDPC, 2017YFC0803300, 2018YFC0809700Funding details: National Office for Philosophy and Social Sciences, NPOPSS, 17AGL026Funding text 1: This work is funded by National Key R&D Program of China (No.2018YFC0809700, No.2017YFC0803300), National Natural Science Foundation of China (No.71804083, No.91646101, No.71673158), and National Social Science Foundation of China (No. 17AGL026).References: Xiao, B., Benbasat, I., E-commerce product recommendation agents: Use, characteristics, and impact (2007) MIS Quarterly, 31 (1), pp. 137-209; Häubl, G., Murray, K.B., Double agents: Assessing the role of electronic product recommendation systems (2006) Sloan Management Review, 47 (3), pp. 8-12; Lee, D.H., Brusilovsky, P., Improving personalized recommendations using community membership information (2017) Information Processing & Management, 53 (5), pp. 1201-1214; 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