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

doi:10.1287/ijoc.2020.1020

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 互联网大数据与信息安全

	Location-Aware Real-Time Recommender Systems for Brick-and-Mortar Retailers
	Zeng, Daniel1 ; Liu, Yong 2; Yan, Ping 3; Yang, Yanwu 4
发表期刊	INFORMS JOURNAL ON COMPUTING
ISSN	1091-9856
	2021-02-25
页码	17
通讯作者	Yang, Yanwu(yangyanwu.isec@gmail.com)
摘要	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.
关键词	recommender systems location-aware recommendation brick-and-mortar stores
DOI	10.1287/ijoc.2020.1020
关键词[WOS]	MODEL ; SIMILARITY ; FRAMEWORK ; PURCHASE ; PATH ; PERSONALIZATION ; CLICKSTREAM ; NETWORKS ; SEARCH
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[71621002] ; National Natural Science Foundation of China[71672067] ; National Natural Science Foundation of China[71328202] ; National Natural Science Foundation of China[71728007] ; Ministry of Science and Technology[2016QY02D0305] ; Key Research Program of Chinese Academy of Sciences[ZDRW-XH-2017-3] ; Marketing Science Institute, Cambridge, Massachusetts[4-1656]
项目资助者	National Natural Science Foundation of China ; Ministry of Science and Technology ; Key Research Program of Chinese Academy of Sciences ; Marketing Science Institute, Cambridge, Massachusetts
WOS研究方向	Computer Science ; Operations Research & Management Science
WOS类目	Computer Science, Interdisciplinary Applications ; Operations Research & Management Science
WOS记录号	WOS:000709029000001
出版者	INFORMS
七大方向——子方向分类	推荐系统
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/46225
专题	多模态人工智能系统全国重点实验室_互联网大数据与信息安全
通讯作者	Yang, Yanwu
作者单位	1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Univ Arizona, Eller Coll Management, Tucson, AZ 85721 USA 3.Salesforcecom Inc, San Francisco, CA 94105 USA 4.Huazhong Univ Sci & Technol, Sch Management, Wuhan 430074, Peoples R China
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Zeng, Daniel,Liu, Yong,Yan, Ping,et al. Location-Aware Real-Time Recommender Systems for Brick-and-Mortar Retailers[J]. INFORMS JOURNAL ON COMPUTING,2021:17.
APA	Zeng, Daniel,Liu, Yong,Yan, Ping,&Yang, Yanwu.(2021).Location-Aware Real-Time Recommender Systems for Brick-and-Mortar Retailers.INFORMS JOURNAL ON COMPUTING,17.
MLA	Zeng, Daniel,et al."Location-Aware Real-Time Recommender Systems for Brick-and-Mortar Retailers".INFORMS JOURNAL ON COMPUTING (2021):17.