TY - GEN
T1 - How to Find Appropriate Automobile Exhibition Halls
T2 - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
AU - Guo, Danhuai
AU - Zhu, Yingqiu
AU - Xu, Wei
AU - Ge, Yong
AU - Zhou, Yuanchun
AU - Li, Jianhui
N1 - Funding Information:
This work was supported in part by 973 Project (No. 2012CB316205 ), National Natural Science Foundation of China (Nos. 41371386 , 91324019 , 91224006 , 91546111 ), Humanities and Social Sciences Foundation of the Ministry of Education (No. 14YJA630075 ), Beijing Nova Program (No. Z131101000413058 ), and the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China (No. 15XNLQ08 )
Publisher Copyright:
© 2015 IEEE.
PY - 2016/1/29
Y1 - 2016/1/29
N2 - This paper proposes a novel recommendation service to help visitors to find their proper automobile exhibition halls for auto show. In the proposed method, both temporal and spatial features of visitors are first considered to construct their profiling, and then extract their interests based on visitors' clustering. Finally, highly desired exhibition halls are personalized recommended to proper visitor. The proposed recommender system consists of three modules including relevance module, quality module and integration module. The relevance module is developed to measure the relationship of an automobile exhibition and a visitor, while the quality module is constructed to analyze the quality of each automobile exhibition. The integration module is to combine two modules above for appropriate automobile exhibition. The proposed approach is well validated using a real world dataset, and compared with several baseline models. Our experimental results indicate that in terms of the well-known evaluation metrics, the proposed method can achieves more useful and feasible recommendation results, and our finding highlights that the proposed method can help both visitors to find a more appropriate automobile exhibition halls, and manage officers to reduce more management cost.
AB - This paper proposes a novel recommendation service to help visitors to find their proper automobile exhibition halls for auto show. In the proposed method, both temporal and spatial features of visitors are first considered to construct their profiling, and then extract their interests based on visitors' clustering. Finally, highly desired exhibition halls are personalized recommended to proper visitor. The proposed recommender system consists of three modules including relevance module, quality module and integration module. The relevance module is developed to measure the relationship of an automobile exhibition and a visitor, while the quality module is constructed to analyze the quality of each automobile exhibition. The integration module is to combine two modules above for appropriate automobile exhibition. The proposed approach is well validated using a real world dataset, and compared with several baseline models. Our experimental results indicate that in terms of the well-known evaluation metrics, the proposed method can achieves more useful and feasible recommendation results, and our finding highlights that the proposed method can help both visitors to find a more appropriate automobile exhibition halls, and manage officers to reduce more management cost.
KW - Auto show
KW - automobile exhibition halls
KW - profiling
KW - recommendation
UR - http://www.scopus.com/inward/record.url?scp=84964754749&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84964754749&partnerID=8YFLogxK
U2 - 10.1109/ICDMW.2015.171
DO - 10.1109/ICDMW.2015.171
M3 - Conference contribution
AN - SCOPUS:84964754749
T3 - Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
SP - 1025
EP - 1030
BT - Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
A2 - Wu, Xindong
A2 - Tuzhilin, Alexander
A2 - Xiong, Hui
A2 - Dy, Jennifer G.
A2 - Aggarwal, Charu
A2 - Zhou, Zhi-Hua
A2 - Cui, Peng
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 November 2015 through 17 November 2015
ER -