Commercial Site Recommendation Based on Neural Collaborative Filtering

被引:8
作者
Li, Nuo [1 ]
Guo, Bin [1 ]
Liu, Yan [1 ]
Jing, Yao [1 ]
Ouyang, Yi [1 ]
Yu, Zhiwen [1 ]
机构
[1] Northwestern Polytech Univ, 128 Youyi West Rd, Xian, Shaanxi, Peoples R China
来源
PROCEEDINGS OF THE 2018 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING AND PROCEEDINGS OF THE 2018 ACM INTERNATIONAL SYMPOSIUM ON WEARABLE COMPUTERS (UBICOMP/ISWC'18 ADJUNCT) | 2018年
关键词
Commercial site recommendation; neural collaborative filtering; recommendation system;
D O I
10.1145/3267305.3267592
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Commercial site recommendation based on big data is one of the innovative applications in the new retail era. Recently, most studies utilize regression analysis or collaborative filtering to recommend the optimal site based on some features extracted from commercial data, geographic data and other heterogeneous data. Compared to manual features which could not be well-defined, deep learning is able to automatically extract features and give nonlinear and in-depth description of the relationship between variables. Therefore, this paper applies deep learning to the study of commercial site recommendation. We firstly study the usage of NeuMF, a neural collaborative filtering method in commercial site recommendation. Then we propose NeuMF-RS method based on NeuMF method. Finally, we evaluate our proposed model on a real-world dataset collected from Dianping. com. The results indicate that NeuMF-RS outperforms the state-of-the-art methods in commercial site recommendation.
引用
收藏
页码:138 / 141
页数:4
相关论文
共 2 条
[1]  
Bin Guo, 2017, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, V1, DOI 10.1145/3161411
[2]   Neural Collaborative Filtering [J].
He, Xiangnan ;
Liao, Lizi ;
Zhang, Hanwang ;
Nie, Liqiang ;
Hu, Xia ;
Chua, Tat-Seng .
PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), 2017, :173-182