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Paper Abstract and Keywords
Presentation 2022-08-04 13:10
Research on Content Selection Manner by Predicting Users' Interests Focusing on Acquired Web Similarity
Takeshi Tsuchiya (Tokyo International Univ.), Rika Misawa, Ryuichi Mochizuki, Tetsuyasu Yamada, Hiroo Hirose (SUS) IN2022-15
Abstract (in Japanese) (See Japanese page) 
(in English) This research proposes content selection methods using the characteristics of a user's most recent web content history to predict the current user's interests. Conventional methods involve identifying users on web services and deriving their interests by using similarities between their behavior patterns and those of users in the past, but this is considered to be a problem due to the acquisition of user information, including personal information such as web behavior and history, by third parties.
The proposed method constructs a combinable feature model named fog model analyzing the features of the acquired web content, and combines it with the user's history. The fog model is combinable feature model able to predict the characteristics of the user's most recent interests. The proposed method provides information similar to the user's interests from the most similar candidate contents.
Keyword (in Japanese) (See Japanese page) 
(in English) Users' Interest / web targetting / distributed learning / / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 146, IN2022-15, pp. 7-10, Aug. 2022.
Paper # IN2022-15 
Date of Issue 2022-07-28 (IN) 
ISSN 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 IN2022-15

Conference Information
Committee IN CCS  
Conference Date 2022-08-04 - 2022-08-05 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido University(Centennial Hall) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Network Science, Future Network, Cloud/SDN/Virtualization, Contents Delivery/Contents Exchange, and others 
Paper Information
Registration To IN 
Conference Code 2022-08-IN-CCS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Research on Content Selection Manner by Predicting Users' Interests Focusing on Acquired Web Similarity 
Sub Title (in English)  
Keyword(1) Users' Interest  
Keyword(2) web targetting  
Keyword(3) distributed learning  
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1st Author's Name Takeshi Tsuchiya  
1st Author's Affiliation Tokyo International University (Tokyo International Univ.)
2nd Author's Name Rika Misawa  
2nd Author's Affiliation Suwa University of Science (SUS)
3rd Author's Name Ryuichi Mochizuki  
3rd Author's Affiliation Suwa University of Science (SUS)
4th Author's Name Tetsuyasu Yamada  
4th Author's Affiliation Suwa University of Science (SUS)
5th Author's Name Hiroo Hirose  
5th Author's Affiliation Suwa University of Science (SUS)
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Speaker Author-1 
Date Time 2022-08-04 13:10:00 
Presentation Time 20 minutes 
Registration for IN 
Paper # IN2022-15 
Volume (vol) vol.122 
Number (no) no.146 
Page pp.7-10 
#Pages 4 
Date of Issue 2022-07-28 (IN) 

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