Personalizing large-scale text classification by modeling individual differences

Jungho Lee; Byung Ju Choi; Yeachan Kim; Kang Min Kim; Woo Jong Ryu; Sangkeun Lee

doi:10.1145/3341105.3374075

Personalizing large-scale text classification by modeling individual differences

Jungho Lee, Byung Ju Choi, Yeachan Kim, Kang Min Kim, Woo Jong Ryu, Sangkeun Lee

Korea University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Large-scale text classification is used to organize and subsequently, analyze textual information into a variety of topics effectively. However, most of existing large-scale text classification models tend to draw similar classification results without accounting for the differences in individual perceptions, as may be discernable through the text semantics based on distinct human characteristics. In this paper, we propose a personalized large-scale text classification model, which factors in these individual differences when classifying data.

Original language	English
Title of host publication	35th Annual ACM Symposium on Applied Computing, SAC 2020
Publisher	Association for Computing Machinery
Pages	900-902
Number of pages	3
ISBN (Electronic)	9781450368667
DOIs	https://doi.org/10.1145/3341105.3374075
State	Published - 30 Mar 2020
Event	35th Annual ACM Symposium on Applied Computing, SAC 2020 - Brno, Czech Republic Duration: 30 Mar 2020 → 3 Apr 2020

Publication series

Name	Proceedings of the ACM Symposium on Applied Computing

Conference

Conference	35th Annual ACM Symposium on Applied Computing, SAC 2020
Country/Territory	Czech Republic
City	Brno
Period	30/03/20 → 3/04/20

Bibliographical note

Publisher Copyright:
© 2020 Owner/Author.

Keywords

Large-scale text classification
Personalization
User modeling

Access to Document

10.1145/3341105.3374075

Cite this

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title = "Personalizing large-scale text classification by modeling individual differences",

abstract = "Large-scale text classification is used to organize and subsequently, analyze textual information into a variety of topics effectively. However, most of existing large-scale text classification models tend to draw similar classification results without accounting for the differences in individual perceptions, as may be discernable through the text semantics based on distinct human characteristics. In this paper, we propose a personalized large-scale text classification model, which factors in these individual differences when classifying data.",

keywords = "Large-scale text classification, Personalization, User modeling",

author = "Jungho Lee and Choi, {Byung Ju} and Yeachan Kim and Kim, {Kang Min} and Ryu, {Woo Jong} and Sangkeun Lee",

note = "Publisher Copyright: {\textcopyright} 2020 Owner/Author.; 35th Annual ACM Symposium on Applied Computing, SAC 2020 ; Conference date: 30-03-2020 Through 03-04-2020",

year = "2020",

month = mar,

day = "30",

doi = "10.1145/3341105.3374075",

language = "English",

series = "Proceedings of the ACM Symposium on Applied Computing",

publisher = "Association for Computing Machinery",

pages = "900--902",

booktitle = "35th Annual ACM Symposium on Applied Computing, SAC 2020",

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Lee, J, Choi, BJ, Kim, Y, Kim, KM, Ryu, WJ & Lee, S 2020, Personalizing large-scale text classification by modeling individual differences. in 35th Annual ACM Symposium on Applied Computing, SAC 2020. Proceedings of the ACM Symposium on Applied Computing, Association for Computing Machinery, pp. 900-902, 35th Annual ACM Symposium on Applied Computing, SAC 2020, Brno, Czech Republic, 30/03/20. https://doi.org/10.1145/3341105.3374075

Personalizing large-scale text classification by modeling individual differences. / Lee, Jungho; Choi, Byung Ju; Kim, Yeachan et al.
35th Annual ACM Symposium on Applied Computing, SAC 2020. Association for Computing Machinery, 2020. p. 900-902 (Proceedings of the ACM Symposium on Applied Computing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Lee, Sangkeun

PY - 2020/3/30

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AB - Large-scale text classification is used to organize and subsequently, analyze textual information into a variety of topics effectively. However, most of existing large-scale text classification models tend to draw similar classification results without accounting for the differences in individual perceptions, as may be discernable through the text semantics based on distinct human characteristics. In this paper, we propose a personalized large-scale text classification model, which factors in these individual differences when classifying data.

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KW - Personalization

KW - User modeling

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M3 - Conference contribution

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ER -

Personalizing large-scale text classification by modeling individual differences

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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