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2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2016)
San Francisco, CA, USA
Aug. 18, 2016 to Aug. 21, 2016
ISBN: 978-1-5090-2847-4
pp: 402-405
Xiaowei Jia , University of Minnesota
Xiaoyi Li , The State University of New York at Buffalo
Kang Li , The State University of New York at Buffalo
Vishrawas Gopalakrishnan , The State University of New York at Buffalo
Guangxu Xun , The State University of New York at Buffalo
Aidong Zhang , The State University of New York at Buffalo
ABSTRACT
The development of social networks has not only improved the online experience, but also stimulated the advances in knowledge mining so as to assist people in planning their offline social events. Users can explore their favorite events, such as celebrations and symposiums, through the pictures and the posts from their friends on social networks. An effective event recommendation can offer great convenience for both event organizers and participants, which yet remains extremely challenging due to a wide range of practical concerns. In this paper we propose a novel recommendation framework, which combines the information from multiple sources and establishes a connection between the online knowledge and the event participation.
INDEX TERMS
Training, Social network services, Feature extraction, Collaboration, Tensile stress, Gaussian noise, Computational modeling
CITATION

X. Jia, X. Li, K. Li, V. Gopalakrishnan, G. Xun and A. Zhang, "Collaborative restricted Boltzmann machine for social event recommendation," 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), San Francisco, CA, USA, 2016, pp. 402-405.
doi:10.1109/ASONAM.2016.7752265
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