Knowledge Commons of Institute of Automation,CAS
Location-Aware Real-Time Recommender Systems for Brick-and-Mortar Retailers | |
Zeng, Daniel1; Liu, Yong2; Yan, Ping3; Yang, Yanwu4 | |
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 | |
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. |
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