Toward effortless TV-to-Online (T2O) experience: A novel metric learning approach

Luo, Y; Wen, Y; Tao, D; Fu, Q

Toward effortless TV-to-Online (T2O) experience: A novel metric learning approach

Luo, Y Wen, Y Tao, D

Fu, Q

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Publication Type:: Conference Proceeding
Citation:: 2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings, 2016
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Luo, Y	en_US
dc.contributor.author	Wen, Y	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Fu, Q	en_US
dc.date.issued	2016-01-01	en_US
dc.identifier.citation	2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings, 2016	en_US
dc.identifier.isbn	9781509013289	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126314
dc.description.abstract	© 2016 IEEE. Shopping of the same or similar types of products as shown in the online TV programs has been highly desired by many people, especially the youth. To meet this eminent market need, we develop a prototype system to enable effortless TV-to-Online (T2O) experience. A key component of this system is the product search that maps specific items embedded in the video into a list of online merchants. The search performance mainly depends on the estimation of the distance or similarity between the queried item and all the curated items in the database. The simple Euclidean (EU) distance cannot capture the data characteristics, we therefore introduce distance metric learning (DML) to improve the distance estimation. Traditional DML methods only utilize the side information (e.g., similar/dissimilar constraints or relevance/irrelevance judgements) in the target domain, and may fail when the side information is scarce. Transfer metric learning (TML) can be adopted to leverage the side information from related domains. In this paper, we treat each domain equally and propose a novel Ranking-based Heterogeneous Multi-Task Metric Learning (RHMTML) framework, which adopts ranking-based loss, so the learned metric is particularly suitable for search. Extensive experiments demonstrate the effectiveness of our proposed method. We foresee that our approach would transform the emerging T2O trend in both TV and online video market.	en_US
dc.relation.ispartof	2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings	en_US
dc.relation.isbasedon	10.1109/GLOCOM.2016.7842335	en_US
dc.title	Toward effortless TV-to-Online (T2O) experience: A novel metric learning approach	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. Shopping of the same or similar types of products as shown in the online TV programs has been highly desired by many people, especially the youth. To meet this eminent market need, we develop a prototype system to enable effortless TV-to-Online (T2O) experience. A key component of this system is the product search that maps specific items embedded in the video into a list of online merchants. The search performance mainly depends on the estimation of the distance or similarity between the queried item and all the curated items in the database. The simple Euclidean (EU) distance cannot capture the data characteristics, we therefore introduce distance metric learning (DML) to improve the distance estimation. Traditional DML methods only utilize the side information (e.g., similar/dissimilar constraints or relevance/irrelevance judgements) in the target domain, and may fail when the side information is scarce. Transfer metric learning (TML) can be adopted to leverage the side information from related domains. In this paper, we treat each domain equally and propose a novel Ranking-based Heterogeneous Multi-Task Metric Learning (RHMTML) framework, which adopts ranking-based loss, so the learned metric is particularly suitable for search. Extensive experiments demonstrate the effectiveness of our proposed method. We foresee that our approach would transform the emerging T2O trend in both TV and online video market.

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http://hdl.handle.net/10453/126314