Empirical research on the impact of personalized recommendation diversity
- Lin Zhang, Tsinghua University, Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing, China
- Qiang Yan, School of Economics and Management, Beijing University of Posts and Telecommunicaitons, Beijing, China
- Junqiang Lu, Beijing University of Posts and Telecommunications, Office of Procurement and Tendering, Beijing University of Posts and Telecommunications, Beijing, China
- Yongqiang Chen, Department of Mechanics and Engineering Science,College of Engineering, Peking University, Beijing, China
- Yi Liu, Tsinghua University, Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing, China
AbstractPersonalized 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.
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