报告时间：2024年11月9日（星期六）上午9: 30-11: 00

报告地点：管理学院新大楼925会议室

报告人：李喜彤 教授

工作单位：法国巴黎HEC商学院

举办单位：合肥工业大学管理学院

报告简介：

Product recommendations can benefit consumers’ online product search via multiple underlying mechanisms, such as showing products that offer them high value, facilitating navigation on the website, or exposing more product information. However, it is unclear ex ante which is the primary underlying mechanism that drives the benefits of product recommendations to consumers. We conducted a randomized field experiment to estimate the benefits of an item-based collaborative filtering (CF) recommendation system to consumers. We collect unique data on the affinity scores computed by an item-based CF algorithm to develop measures of a product’s net value and horizontal (taste) fit for consumers. Our results indicate that product recommendations help consumers search for higher-value products that are lower priced, fit their tastes better, or both. Besides that, we find that the ability to find higher-value products (rather than easy navigation or exposure to more product information) is the primary driver for consumers’ higher purchase probabilities under recommendations. We further find a higher benefit of recommendations in product categories with higher price dispersion and heterogeneity in consumers’ tastes, providing additional evidence for the lower price and better horizontal fit mechanisms. Finally, we find that when made available, consumers substitute their usage of other search tools on the website with product recommendations. Our findings have important implications for online retailers, policymakers, regulators, and item-based CF recommendation system design.

报告人简介：

李喜彤，法国巴黎HEC商学院教授，研究兴趣包括社交媒体、众筹、数字营销、在线教育、算法及人工智能等领域，主要研究方法包括应用计量经济分析、田野实验和实验室实验等。他的研究成果已在许多顶级期刊上发表，包括Management Science、Information Systems Research、MIS Quarterly、Production and Operations Management等。