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Title:
Who will follow your shop? Exploiting multiple information sources in finding followers
Author: Wu, Liang (2) ; Chin, Alvin (1) ; Xu, Guandong (3) ; Du, Liang (5) ; Wang, Xia (4) ; Meng, Kangjian (4) ; Guo, Yonggang (4) ; Zhou, Yuanchun (2)
Conference Name: 18th International Conference on Database Systems for Advanced Applications, DASFAA 2013
Conference Date: April 22, 2013 - April 25, 2013
Issued Date: 2013
Conference Place: Wuhan, China
Publish Place: Springer Verlag, Tiergartenstrasse 17, Heidelberg, D-69121, Germany
Indexed Type: EI
ISSN: 3029743
ISBN: 9783642374494
Department: (1) Xpress Internet Services, Nokia, Beijing, China; (2) Computer Network Information Center, Chinese Academy of Sciences, China; (3) Advanced Analytics Institute, University of Technology Sydney, Australia; (4) Beijing NoyaXe Technologies Co. Ltd., China; (5) Institute of Software, Chinese Academy of Sciences, China
Abstract: WuXianGouXiang is an O2O(offline to online and vice versa)-based mobile application that recommends the nearby coupons and deals for users, by which users can also follow the shops they are interested in. If the potential followers of a shop can be discovered, the merchant's targeted advertising can be more effective and the recommendations for users will also be improved. In this paper, we propose to predict the link relations between users and shops based on the following behavior. In order to better model the characteristics of the shops, we first adopt Topic Modeling to analyze the semantics of their descriptions and then propose a novel approach, named INtent Induced Topic Search (INITS) to update the hidden topics of the shops with and without a description. In addition, we leverage the user logs and search engine results to get the similarity between users and shops. Then we adopt the latent factor model to calculate the similarity between users and shops, in which we use the multiple information sources to regularize the factorization. The experimental results demonstrate that the proposed approach is effective for detecting followers of the shops and the INITS model is useful for shop topic inference. © Springer-Verlag 2013.
English Abstract: WuXianGouXiang is an O2O(offline to online and vice versa)-based mobile application that recommends the nearby coupons and deals for users, by which users can also follow the shops they are interested in. If the potential followers of a shop can be discovered, the merchant's targeted advertising can be more effective and the recommendations for users will also be improved. In this paper, we propose to predict the link relations between users and shops based on the following behavior. In order to better model the characteristics of the shops, we first adopt Topic Modeling to analyze the semantics of their descriptions and then propose a novel approach, named INtent Induced Topic Search (INITS) to update the hidden topics of the shops with and without a description. In addition, we leverage the user logs and search engine results to get the similarity between users and shops. Then we adopt the latent factor model to calculate the similarity between users and shops, in which we use the multiple information sources to regularize the factorization. The experimental results demonstrate that the proposed approach is effective for detecting followers of the shops and the INITS model is useful for shop topic inference. © Springer-Verlag 2013.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/16674
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Wu, Liang ,Chin, Alvin ,Xu, Guandong ,et al. Who will follow your shop? Exploiting multiple information sources in finding followers[C]. 见:18th International Conference on Database Systems for Advanced Applications, DASFAA 2013. Wuhan, China. April 22, 2013 - April 25, 2013.
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