An evaluation of page segment recommendation system using user's notes and N-Gram models

© 2015 IEEE. Web content searching is a daily activity of almost everyone. Often, it occurs several times a day. A number of people need to make sense out of a huge amount of webpages in order to complete their jobs. Many others also have to rely on it. A number of research works in sensemaking have...

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Main Authors:	Burin Thunnom, Lachana Ramingwong
Format:	Conference Proceeding
Published:	2018
Subjects:	Agricultural and Biological Sciences
Online Access:	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84961783266&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/44066
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Institution:	Chiang Mai University

id	th-cmuir.6653943832-44066
record_format	dspace
spelling	th-cmuir.6653943832-440662018-04-25T07:45:15Z An evaluation of page segment recommendation system using user's notes and N-Gram models Burin Thunnom Lachana Ramingwong Agricultural and Biological Sciences © 2015 IEEE. Web content searching is a daily activity of almost everyone. Often, it occurs several times a day. A number of people need to make sense out of a huge amount of webpages in order to complete their jobs. Many others also have to rely on it. A number of research works in sensemaking have demonstrated the needs for supporting tools in web content searching. In this paper, NorCost, a system that recommends relevant page segments, is proposed. The system emphasizes helping people to complete their sensemaking tasks without having to go through every detail of the webpage themselves as such tasks could takes long time to finish. The evaluation of NorCost is carried out to assess its accuracy as well as time taken to process the recommendation. 2018-01-24T04:37:40Z 2018-01-24T04:37:40Z 2015-11-20 Conference Proceeding 2-s2.0-84961783266 10.1109/ICIEV.2015.7333990 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84961783266&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/44066
institution	Chiang Mai University
building	Chiang Mai University Library
country	Thailand
collection	CMU Intellectual Repository
topic	Agricultural and Biological Sciences
spellingShingle	Agricultural and Biological Sciences Burin Thunnom Lachana Ramingwong An evaluation of page segment recommendation system using user's notes and N-Gram models
description	© 2015 IEEE. Web content searching is a daily activity of almost everyone. Often, it occurs several times a day. A number of people need to make sense out of a huge amount of webpages in order to complete their jobs. Many others also have to rely on it. A number of research works in sensemaking have demonstrated the needs for supporting tools in web content searching. In this paper, NorCost, a system that recommends relevant page segments, is proposed. The system emphasizes helping people to complete their sensemaking tasks without having to go through every detail of the webpage themselves as such tasks could takes long time to finish. The evaluation of NorCost is carried out to assess its accuracy as well as time taken to process the recommendation.
format	Conference Proceeding
author	Burin Thunnom Lachana Ramingwong
author_facet	Burin Thunnom Lachana Ramingwong
author_sort	Burin Thunnom
title	An evaluation of page segment recommendation system using user's notes and N-Gram models
title_short	An evaluation of page segment recommendation system using user's notes and N-Gram models
title_full	An evaluation of page segment recommendation system using user's notes and N-Gram models
title_fullStr	An evaluation of page segment recommendation system using user's notes and N-Gram models
title_full_unstemmed	An evaluation of page segment recommendation system using user's notes and N-Gram models
title_sort	evaluation of page segment recommendation system using user's notes and n-gram models
publishDate	2018
url	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84961783266&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/44066
_version_	1681422490436370432

An evaluation of page segment recommendation system using user's notes and N-Gram models

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