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

doi:10.1287/ijoc.2020.1020

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 互联网大数据与安全信息学研究中心

	Location-Aware Real-Time Recommender Systems for Brick-and-Mortar Retailers
	Zeng, Daniel 2; Liu, Yong 1; Yan, Ping 3; Yang, Yanwu 4
刊名	INFORMS JOURNAL ON COMPUTING
	2021-02-25
页码	17
关键词	recommender systems location-aware recommendation brick-and-mortar stores
ISSN号	1091-9856
DOI	10.1287/ijoc.2020.1020
通讯作者	Yang, Yanwu(yangyanwu.isec@gmail.com)
英文摘要	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.
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]
WOS关键词	MODEL ; SIMILARITY ; FRAMEWORK ; PURCHASE ; PATH ; PERSONALIZATION ; CLICKSTREAM ; NETWORKS ; SEARCH
WOS研究方向	Computer Science ; Operations Research & Management Science
语种	英语
出版者	INFORMS
WOS记录号	WOS:000709029000001
资助机构	National Natural Science Foundation of China ; Ministry of Science and Technology ; Key Research Program of Chinese Academy of Sciences ; Marketing Science Institute, Cambridge, Massachusetts
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/46224]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心
通讯作者	Yang, Yanwu
作者单位	1.Univ Arizona, Eller Coll Management, Tucson, AZ 85721 USA 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 3.Salesforcecom Inc, San Francisco, CA 94105 USA 4.Huazhong Univ Sci & Technol, Sch Management, Wuhan 430074, Peoples R China
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.