Location-Aware Real-Time Recommender Systems for Brick-and-Mortar Retailers

Daniel Zeng (), Yong Liu (), Ping Yan () and Yanwu Yang ()
Additional contact information
Daniel Zeng: Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Yong Liu: Eller College of Management, University of Arizona, Tucson, Arizona 85721
Ping Yan: Salesforce.com, Inc., San Francisco, California 94105
Yanwu Yang: School of Management, Huazhong University of Science and Technology, Wuhan 430074, China

INFORMS Journal on Computing, 2021, vol. 33, issue 4, 1608-1623

Abstract: Providing real-time product recommendations based on consumer profiles and purchase history is a successful marketing strategy in online retailing. However, brick-and-mortar (BAM) retailers have yet to utilize this important promotional strategy because it is difficult to predict consumer preferences as they travel in a physical space but remain anonymous and unidentifiable until checkout. In this paper, we develop such a recommender approach by leveraging the consumer shopping path information generated by radio frequency identification technologies. The system relies on spatial-temporal pattern discovery that measures the similarity between paths and recommends products based on measured similarity. We use a real-world retail data set to demonstrate the feasibility of this real-time recommender system and show that our approach outperforms benchmark methods in key recommendation metrics. Conceptually, this research provides generalizable insights on the correlation between spatial movement and consumer preference. It makes a strong case that the emerging location and path data and the spatial-temporal pattern discovery methods can be effectively utilized for implementable marketing strategies. Managerially, it provides one of the first real-time recommender systems for BAM retailers. Our approach can potentially become the core of the next-generation intelligent shopping environment in which the stores customize marketing efforts to provide real-time, location-aware recommendations.

Keywords: recommender systems; location-aware recommendation; brick-and-mortar stores (search for similar items in EconPapers)
Date: 2021
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