Offline Shop Recommendation based On Online Shopping History

被引：0
|
作者：
Mo, Chongji [1 ]
Chen, Congcong [1 ]
机构：
[1] Shanghai Jiao Tong Univ, Sch Software, Shanghai 200240, Peoples R China
来源：
PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON EDUCATION TECHNOLOGY, MANAGEMENT AND HUMANITIES SCIENCE (ETMHS 2015) | 2015年 / 27卷
关键词：
Recommender system; O2O;
D O I：
暂无
中图分类号：
G40 [教育学];
学科分类号：
040101 ; 120403 ;
摘要：
Most existing POI recommender systems is facing the problem of sparsely of data. In this paper, an O2O (Online to Offline) recommendation method is proposed, which can take advantage of users' online shopping history data. User behavior is converted to user preference, modeled as user and interest-term matrix. Based on the model, the result list of offline shop is recommended by O2O recommendation algorithm. The algorithm is implemented, and experiments on the real dataset show the feasibility of the method.
引用
收藏
页码：962 / 965
页数：4
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