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 |
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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 | |
Publication status | Published - 2020 Mar 30 |
Event | 35th Annual ACM Symposium on Applied Computing, SAC 2020 - Brno, Czech Republic Duration: 2020 Mar 30 → 2020 Apr 3 |
Publication series
Name | Proceedings of the ACM Symposium on Applied Computing |
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Conference
Conference | 35th Annual ACM Symposium on Applied Computing, SAC 2020 |
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Country/Territory | Czech Republic |
City | Brno |
Period | 20/3/30 → 20/4/3 |
Bibliographical note
Publisher Copyright:© 2020 Owner/Author.
Keywords
- Large-scale text classification
- Personalization
- User modeling
ASJC Scopus subject areas
- Software