Effects of product portfolios and recommendation timing in the efficiency of personalized recommendation

Qiang Yan(Beijing University of Posts and Telecommunications), Lin Zhang(Beijing University of Posts and Telecommunications), Yuxia Li(Beijing University of Posts and Telecommunications), Shuang Wu(Beijing University of Posts and Telecommunications), Tingting Sun(Beijing University of Posts and Telecommunications), Lingli Wang(Beijing University of Posts and Telecommunications), Hejie Chen(Beijing University of Posts and Telecommunications)
Journal of Consumer Behaviour
June 14, 2016
Cited by 24

Abstract

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 and efficiency of the recommendation algorithms or are more concerned on ways to reduce perceived risks and thus increase consumer satisfaction. Unlike these studies, our study begins from the decision‐making process of consumers, using consumers' two‐stage decision‐making system and preference inconsistency theory as a basis, to reveal the mechanisms involved in consumers' acceptance of recommendations. This paper analyzes the effect of personalized recommendations from two angles, recommendation timing and product portfolio, tries to point out differences in consumer preferences between similar products and related products, and verifies that consumers demand diversity in the recommended content. The study analyzes differences in the acceptance of personalized recommendations between practical products and hedonic products and discovers that recommendations of hedonic products are more effective than that of practical products. Based on the research earlier, the study provides suggestions on how to better plan and operate a personalized recommendation system. Copyright © 2016 John Wiley & Sons, Ltd.


Related Papers

No related papers found

Powered by citation graph analysis