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Ms. Soyeon Oh
Ewha Womans University

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0 Big Data
0 Data Mining
0 Deep Learning
0 Distributed learning
0 Stream Analytics

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Short Biography

Ms. Oh received the B.S. and M.S. degrees in Computer Science and Engineering from Ewha Womans University in 2016 and 2018, respectively. She is now a Ph.D student of Ewha Womans University.

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Journal article
Published: 12 March 2021 in Applied Sciences
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Over the past few years, the number of users of social network services has been exponentially increasing and it is now a natural source of data that can be used by recommendation systems to provide important services to humans by analyzing applicable data and providing personalized information to users. In this paper, we propose an information recommendation technique that enables smart recommendations based on two specific types of analysis on user behaviors, such as the user influence and user activity. The components to measure the user influence and user activity are identified. The accuracy of the information recommendation is verified using Yelp data and shows significantly promising results that could create smarter information recommendation systems.

ACS Style

Minsoo Lee; Soyeon Oh. An Information Recommendation Technique Based on Influence and Activeness of Users in Social Networks. Applied Sciences 2021, 11, 2530 .

AMA Style

Minsoo Lee, Soyeon Oh. An Information Recommendation Technique Based on Influence and Activeness of Users in Social Networks. Applied Sciences. 2021; 11 (6):2530.

Chicago/Turabian Style

Minsoo Lee; Soyeon Oh. 2021. "An Information Recommendation Technique Based on Influence and Activeness of Users in Social Networks." Applied Sciences 11, no. 6: 2530.

Journal article
Published: 30 September 2018 in Genomics & Informatics
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Genomics & Informatics (NLM title abbreviation: Genomics Inform) is the official journal of the Korea Genome Organization. Text corpus for this journal annotated with various levels of linguistic information would be a valuable resource as the process of information extraction requires syntactic, semantic, and higher levels of natural language processing. In this study, we publish our new corpus called GNI Corpus version 1.0, extracted and annotated from full texts of Genomics & Informatics, with NLTK (Natural Language ToolKit)-based text mining script. The preliminary version of the corpus could be used as a training and testing set of a system that serves a variety of functions for future biomedical text mining.

ACS Style

So-Yeon Oh; Ji-Hyeon Kim; Seo-Jin Kim; Hee-Jo Nam; Hyun-Seok Park. GNI Corpus Version 1.0: Annotated Full-Text Corpus of Genomics & Informatics to Support Biomedical Information Extraction. Genomics & Informatics 2018, 16, 75 -77.

AMA Style

So-Yeon Oh, Ji-Hyeon Kim, Seo-Jin Kim, Hee-Jo Nam, Hyun-Seok Park. GNI Corpus Version 1.0: Annotated Full-Text Corpus of Genomics & Informatics to Support Biomedical Information Extraction. Genomics & Informatics. 2018; 16 (3):75-77.

Chicago/Turabian Style

So-Yeon Oh; Ji-Hyeon Kim; Seo-Jin Kim; Hee-Jo Nam; Hyun-Seok Park. 2018. "GNI Corpus Version 1.0: Annotated Full-Text Corpus of Genomics & Informatics to Support Biomedical Information Extraction." Genomics & Informatics 16, no. 3: 75-77.