Exploring Content-based Video Relevance for Video Click-Through Rate Prediction

Wang, X; Du, Y; Zhang, L; Li, X; Zhang, M; Dong, J

Exploring Content-based Video Relevance for Video Click-Through Rate Prediction

Wang, X Du, Y Zhang, L Li, X Zhang, M Dong, J

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Publisher:: ACM
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 27th ACM International Conference on Multimedia, 2019, pp. 2602 - 2606
Issue Date:: 2019-10

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Field	Value	Language
dc.contributor.author	Wang, X	en_US
dc.contributor.author	Du, Y	en_US
dc.contributor.author	Zhang, L	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	Zhang, M	en_US
dc.contributor.author	Dong, J	en_US
dc.date	2019-10-21	en_US
dc.date.issued	2019-10	en_US
dc.identifier.citation	Proceedings of the 27th ACM International Conference on Multimedia, 2019, pp. 2602 - 2606	en_US
dc.identifier.isbn	9781450368896	en_US
dc.identifier.uri	http://hdl.handle.net/10453/139775
dc.description.abstract	This paper describes our solution for the Hulu Challenge. To answer the challenge, we introduce two content-based models, namely, Cascading Mapping Network (CMN) and Relevant-Enhanced Deep Interest Network (REDIN). CMN predicts video Click-Through Rate (CTR) by predicting content-based video relevance. REDIN mainly improves the popular Deep Interest Network by adding explicit video relevance constraint, which provides guidance for low-level video feature learning thus helpful for CTR prediction. Based on the two models, our solution obtains Area Under Curve (AUC) score of 0.6022 and 0.6155 on the TV-shows and Movie track respectively. What is more, we are one of the only two teams giving scores of over 0.6 on both tracks. The results justify the effectiveness and stability of our proposed solution.	en_US
dc.publisher	ACM	en_US
dc.relation.ispartof	Proceedings of the 27th ACM International Conference on Multimedia	en_US
dc.relation.ispartof	MM '19: The 27th ACM International Conference on Multimedia	en_US
dc.relation.isbasedon	10.1145/3343031.3356053	en_US
dc.rights	© ACM 2019. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 27th ACM International Conference on Multimedia, 2019, pp. 2602 - 2606 http://doi.acm.org/10.1145/3343031.3356053	en_US
dc.title	Exploring Content-based Video Relevance for Video Click-Through Rate Prediction	en_US
dc.type	Conference Proceeding
utslib.location.activity	Nice, France	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/Students
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.finish-date	2019-10-25	en_US
pubs.publication-status	Published	en_US
pubs.start-date	2019-10-21	en_US

Abstract:

This paper describes our solution for the Hulu Challenge. To answer the challenge, we introduce two content-based models, namely, Cascading Mapping Network (CMN) and Relevant-Enhanced Deep Interest Network (REDIN). CMN predicts video Click-Through Rate (CTR) by predicting content-based video relevance. REDIN mainly improves the popular Deep Interest Network by adding explicit video relevance constraint, which provides guidance for low-level video feature learning thus helpful for CTR prediction. Based on the two models, our solution obtains Area Under Curve (AUC) score of 0.6022 and 0.6155 on the TV-shows and Movie track respectively. What is more, we are one of the only two teams giving scores of over 0.6 on both tracks. The results justify the effectiveness and stability of our proposed solution.

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http://hdl.handle.net/10453/139775