How to find appropriate automobile exhibition halls: Towards a personalized recommendation service for auto show

被引:27
作者
Guo, Danhuai [1 ]
Zhu, Yingqiu [1 ]
Xu, Wei [2 ,3 ]
Shang, Shuo [4 ]
Ding, Zhiming [5 ]
机构
[1] Chinese Acad Sci, Sci Data Ctr, Comp Network Informat Ctr, Beijing, Peoples R China
[2] Renmin Univ China, Sch Informat, Beijing, Peoples R China
[3] Renmin Univ China, Smart City Res Ctr, Beijing, Peoples R China
[4] China Univ Petr, Beijing, Peoples R China
[5] Beijing Univ Technol, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
Auto show; Automobile exhibition halls; Recommendation; Profiling;
D O I
10.1016/j.neucom.2016.02.084
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper proposes a novel recommendation methodology to guide visitors to find their proper automobile exhibition halls for auto show. In the proposed method, spatio-temporal features of visitors' behavior are first considered to construct their profiling, and then their interests are extracted based on visitors' clustering. Next, three modules including relevance module, quality module and integration module are developed for ranking visitors' preference of exhibition halls. Finally, highly desired exhibition halls are personalized and recommended to proper visitors. In the proposed modules, 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 recommending 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 achieve 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. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:95 / 101
页数:7
相关论文
共 32 条
[1]   Incorporating contextual information in recommender systems using a multidimensional approach [J].
Adomavicius, G ;
Sankaranarayanan, R ;
Sen, S ;
Tuzhilin, A .
ACM TRANSACTIONS ON INFORMATION SYSTEMS, 2005, 23 (01) :103-145
[2]   Improving Smart Conference Participation Through Socially Aware Recommendation [J].
Asabere, Nana Yaw ;
Xia, Feng ;
Wang, Wei ;
Rodrigues, Joel J. P. C. ;
Basso, Filippo ;
Ma, Jianhua .
IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS, 2014, 44 (05) :689-700
[3]   A novel discriminant criterion based on feature fusion strategy for face recognition [J].
Chen, Wen-Sheng ;
Dai, Xiuli ;
Pan, Binbin ;
Huang, Taiquan .
NEUROCOMPUTING, 2015, 159 :67-77
[4]  
Elkachouchi H., 2005, Proceedings of the Twenty-Second National Radio Science Conference, P173
[5]  
Felfernig A, 2011, RECOMMENDER SYSTEMS HANDBOOK, P187, DOI 10.1007/978-0-387-85820-3_6
[6]   Top-K structural diversity search in large networks [J].
Huang, Xin ;
Cheng, Hong ;
Li, Rong-Hua ;
Qin, Lu ;
Yu, Jeffrey Xu .
VLDB JOURNAL, 2015, 24 (03) :319-343
[7]  
Kaemarungsi K, 2004, PROCEEDINGS OF MOBIQUITOUS 2004, P14
[8]  
Kaemarungsi K, 2004, IEEE INFOCOM SER, P1012
[9]   Algorithm for TOA-based indoor geolocation [J].
Kanaan, M ;
Pahlavan, K .
ELECTRONICS LETTERS, 2004, 40 (22) :1421-1423
[10]   A Strategy of Clustering Modification Directions in Spatial Image Steganography [J].
Li, Bin ;
Wang, Ming ;
Li, Xiaolong ;
Tan, Shunquan ;
Huang, Jiwu .
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2015, 10 (09) :1905-1917