A deep approach for multi-modal user attribute modeling

Huang, X; Yang, Z; Yang, Y; Shen, F; Xie, N; Shen, HT

A deep approach for multi-modal user attribute modeling

Huang, X Yang, Z Yang, Y Shen, F Xie, N Shen, HT

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, 10538 LNCS pp. 217 - 230
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Huang, X	en_US
dc.contributor.author	Yang, Z	en_US
dc.contributor.author	Yang, Y	en_US
dc.contributor.author	Shen, F	en_US
dc.contributor.author	Xie, N	en_US
dc.contributor.author	Shen, HT	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, 10538 LNCS pp. 217 - 230	en_US
dc.identifier.isbn	9783319681542	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127378
dc.description.abstract	© 2017, Springer International Publishing AG. With the explosive growth of user-generated contents (e.g., texts, images and videos) on social networks, it is of great significance to analyze and extract people’s interests from the massive social media data, thus providing more accurate personalized recommendations and services. In this paper, we propose a novel multimodal deep learning algorithm for user profiling, dubbed multi-modal User Attribute Model (mmUAM), which explores the intrinsic semantic correlations across different modalities. Our proposed model is based on Poisson Gamma Belief Network (PGBN), which is a deep learning topic model for count data in documents. By improving PGBN, we succeed in addressing the problem of learning a shared representation between texts and images in order to obtain textual and visual attributes for users. To evaluate the effectiveness of our proposed method, we collect a real dataset from Sina Weibo. Experimental results demonstrate that the proposed algorithm achieves encouraging performance compared with several state-of-the-art methods.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-319-68155-9_17	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A deep approach for multi-modal user attribute modeling	en_US
dc.type	Conference Proceeding
utslib.citation.volume	10538 LNCS	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	10538 LNCS	en_US

Abstract:

© 2017, Springer International Publishing AG. With the explosive growth of user-generated contents (e.g., texts, images and videos) on social networks, it is of great significance to analyze and extract people’s interests from the massive social media data, thus providing more accurate personalized recommendations and services. In this paper, we propose a novel multimodal deep learning algorithm for user profiling, dubbed multi-modal User Attribute Model (mmUAM), which explores the intrinsic semantic correlations across different modalities. Our proposed model is based on Poisson Gamma Belief Network (PGBN), which is a deep learning topic model for count data in documents. By improving PGBN, we succeed in addressing the problem of learning a shared representation between texts and images in order to obtain textual and visual attributes for users. To evaluate the effectiveness of our proposed method, we collect a real dataset from Sina Weibo. Experimental results demonstrate that the proposed algorithm achieves encouraging performance compared with several state-of-the-art methods.

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http://hdl.handle.net/10453/127378