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

文献类型：期刊论文

作者Zeng, Daniel1; Liu, Yong2; Yan, Ping3; Yang, Yanwu4
刊名INFORMS JOURNAL ON COMPUTING
出版日期2021-02-25
页码17
关键词recommender systems location-aware recommendation brick-and-mortar stores
ISSN号1091-9856
DOI10.1287/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.
WOS关键词MODEL ; SIMILARITY ; FRAMEWORK ; PURCHASE ; PATH ; PERSONALIZATION ; CLICKSTREAM ; NETWORKS ; SEARCH
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研究方向Computer Science ; Operations Research & Management Science
语种英语
WOS记录号WOS:000709029000001
出版者INFORMS
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
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.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
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.

入库方式： OAI收割

来源：自动化研究所

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