TY - GEN
T1 - Poster
T2 - 2018 Joint ACM International Conference on Pervasive and Ubiquitous Computing, UbiComp 2018 and 2018 ACM International Symposium on Wearable Computers, ISWC 2018
AU - Li, Nuo
AU - Guo, Bin
AU - Liu, Yan
AU - Jing, Yao
AU - Ouyang, Yi
AU - Yu, Zhiwen
N1 - Publisher Copyright:
© 2018 Copyright is held by the owner/author(s).
PY - 2018/10/8
Y1 - 2018/10/8
N2 - 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.
AB - 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.
KW - Commercial site recommendation
KW - Neural collaborative filtering
KW - Recommendation system
UR - https://www.scopus.com/pages/publications/85058266882
U2 - 10.1145/3267305.3267592
DO - 10.1145/3267305.3267592
M3 - 会议稿件
AN - SCOPUS:85058266882
T3 - UbiComp/ISWC 2018 - Adjunct Proceedings of the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2018 ACM International Symposium on Wearable Computers
SP - 138
EP - 141
BT - UbiComp/ISWC 2018 - Adjunct Proceedings of the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2018 ACM International Symposium on Wearable Computers
PB - Association for Computing Machinery, Inc
Y2 - 8 October 2018 through 12 October 2018
ER -