Topic: When Product Recommendations Fall Short: The Heterogeneous Impact of Product Network Characteristics on Recommender System Performance

Speaker: Wan Xiang, Santa Clara University

Time: September 3, 2024 9:30 am

Venue: EMS 319

Abstract:Recommendation systems create a network of interconnected products on e-commerce websites by showing related products on the focal product pages. We ran a field experiment on a US fashion e-tailer’s website to examine how two core product network characteristics ‒ the focal product’s location in the network and its closeness with the recommended products ‒ jointly influence the recommendation’s effect on total product sales. While we find it beneficial to show either closely or weakly related recommended products for the focal products at the center of the recommendation network, it is helpful to recommend only weakly related products for the focal products at the edges. We provide empirical evidence that consumers seek product variety when exploring focal products at the network’s edges. Thus, offering dissimilar product recommendations to them may better fulfill their variety-seeking needs. Finally, we show that retailers can exploit the heterogeneous impact of product network characteristics to generate recommendation policies to optimize product sales. We show the e-retailer could obtain four percent higher sales under our suggested recommendation policy.

Guest Bio:Wan Xiang, an assistant professor in the Information Systems and Analysis Department at Leavey School of Business, Santa Clara University in Silicon Valley, has a PhD in Information Systems and Operations Management from the University of Florida (2022), an MSc in Management Science and Engineering from Renmin University of China (2017), and a BEng in Engineering Management from Wuhan University (2014). His research interests mainly focus on three areas: algorithmic recommendation systems, artificial intelligence, and blockchain, as well as digital economy. His research findings have been published in top journals such as Management Science and Information Systems Research, and he has received the 2022 Best PhD Dissertation Award from the Association for Information Systems, ranking second.