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Paper Abstract and Keywords
Presentation 2018-03-08 15:30
Banner Click Through Rate Classification Using Deep Neural Convolutional Network
Nicolas Michel (Univ. Of Tokyo), Hayato Sakata (SMN), Keita Kurita, Toshihiko Yamasaki (Univ. Of Tokyo) IMQ2017-43 IE2017-135 MVE2017-85
Abstract (in Japanese) (See Japanese page) 
(in English) In banner advertising, Click Through Rate (CTR) is one of the most important indicators to evaluate one
advertisement?s quality. Advertisers create massive number of banner candidates in empirical ways, then proceed to actual tests
by delivering advertisement to measure each banner?s effectiveness. This process is expensive and therefore our CTR prediction
helps reducing online advertising costs. In this work, we propose a method to classify effective and ineffective advertising
banners based on image processing using state-of-the-art CNN. We first focus only on images then conduct experiments including
metadata (product, advertiser, etc) to increase the CTR prediction accuracy and demonstrate which metadata is the most
influential. Subsequently, each approach is compared to human performance. In the second part of our work, we detect which
parts of the image contribute predominantly to increase the CTR by generating heat maps for each classes. This work leads to a
deeper understanding of a banner advertising success and helps making decisions on how to improve it.
Keyword (in Japanese) (See Japanese page) 
(in English) Convolutional Neural Network / Click Through Rate / Deep Learning / Banner Advertising / Deep Learning / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 484, IE2017-135, pp. 101-106, March 2018.
Paper # IE2017-135 
Date of Issue 2018-03-01 (IMQ, IE, MVE) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF IMQ2017-43 IE2017-135 MVE2017-85

Conference Information
Committee CQ MVE IE IMQ  
Conference Date 2018-03-08 - 2018-03-09 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Industry Support Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Five Senses Media, Cooking and Eating Activities Media, Multimedia, Media Experience, Video Encoding, Image Media Quality, Network Quality and Reliability, etc. (Co-sponsor: Technical Committee on Multimedia on Cooking and Eating Activities (CEA)) 
Paper Information
Registration To IE 
Conference Code 2018-03-CQ-MVE-IE-IMQ 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Banner Click Through Rate Classification Using Deep Neural Convolutional Network 
Sub Title (in English)  
Keyword(1) Convolutional Neural Network  
Keyword(2) Click Through Rate  
Keyword(3) Deep Learning  
Keyword(4) Banner Advertising  
Keyword(5) Deep Learning  
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Keyword(7)  
Keyword(8)  
1st Author's Name Nicolas Michel  
1st Author's Affiliation University Of Tokyo (Univ. Of Tokyo)
2nd Author's Name Hayato Sakata  
2nd Author's Affiliation So-net Media Networks (SMN)
3rd Author's Name Keita Kurita  
3rd Author's Affiliation University Of Tokyo (Univ. Of Tokyo)
4th Author's Name Toshihiko Yamasaki  
4th Author's Affiliation University Of Tokyo (Univ. Of Tokyo)
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Speaker Author-1 
Date Time 2018-03-08 15:30:00 
Presentation Time 25 minutes 
Registration for IE 
Paper # IMQ2017-43, IE2017-135, MVE2017-85 
Volume (vol) vol.117 
Number (no) no.483(IMQ), no.484(IE), no.485(MVE) 
Page pp.101-106 
#Pages 6 
Date of Issue 2018-03-01 (IMQ, IE, MVE) 

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