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- W2202285452 abstract "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." @default.
- W2202285452 created "2016-06-24" @default.
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- W2202285452 date "2015-10-01" @default.
- W2202285452 modified "2023-09-26" @default.
- W2202285452 title "Predicting various types of user attributes in Twitter by using personalized pagerank" @default.
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- W2202285452 doi "https://doi.org/10.1109/bigdata.2015.7364090" @default.
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