Predicting various types of user attributes in Twitter by using personalized pagerank

Kazuya Uesato, Hiroki Asai, Hayato Yamana

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Predicting various types of user-attributes in social networks has become indispensable for personalizing applications since there are many non-disclosed attributes in social networks. However, extracted attributes in existing works are limited to pre-defined types of attributes, which results in no extraction of unexpected-types of attributes. In this paper, we therefore propose a novel method that extracts various, i.e., unlimited, types of attributes by adopting personalized PageRank to a large social network. The experimental results using over 7.9 million of Japanese Twitter-users show that our proposed method successfully extracts four types of attributes per-user in average with 0.841 of MAP@20.

本文言語English
ホスト出版物のタイトルProceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015
編集者Feng Luo, Kemafor Ogan, Mohammed J. Zaki, Laura Haas, Beng Chin Ooi, Vipin Kumar, Sudarsan Rachuri, Saumyadipta Pyne, Howard Ho, Xiaohua Hu, Shipeng Yu, Morris Hui-I Hsiao, Jian Li
出版社Institute of Electrical and Electronics Engineers Inc.
ページ2825-2827
ページ数3
ISBN(電子版)9781479999255
DOI
出版ステータスPublished - 2015 12 22
イベント3rd IEEE International Conference on Big Data, IEEE Big Data 2015 - Santa Clara, United States
継続期間: 2015 10 292015 11 1

出版物シリーズ

名前Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015

Other

Other3rd IEEE International Conference on Big Data, IEEE Big Data 2015
国/地域United States
CitySanta Clara
Period15/10/2915/11/1

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • コンピュータ サイエンスの応用
  • 情報システム
  • ソフトウェア

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