Personalizing large-scale text classification by modeling individual differences

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publication35th Annual ACM Symposium on Applied Computing, SAC 2020
PublisherAssociation for Computing Machinery
Pages900-902
Number of pages3
ISBN (Electronic)9781450368667
DOIs
Publication statusPublished - 2020 Mar 30
Event35th Annual ACM Symposium on Applied Computing, SAC 2020 - Brno, Czech Republic
Duration: 2020 Mar 302020 Apr 3

Publication series

NameProceedings of the ACM Symposium on Applied Computing

Conference

Conference35th Annual ACM Symposium on Applied Computing, SAC 2020
Country/TerritoryCzech Republic
CityBrno
Period20/3/3020/4/3

Bibliographical note

Funding Information:
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2018R1A2A1A05078380).

Publisher Copyright:
© 2020 Owner/Author.

Keywords

  • Large-scale text classification
  • Personalization
  • User modeling

ASJC Scopus subject areas

  • Software

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