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A Two-Phase On-Device Analysis for Gender Prediction of Mobile Users Using Discriminative and Popular Wordsets

모바일 사용자의 성별 예측을 위한 식별 및 인기 단어 집합 기반 2단계 기기 내 분석

Choi, Yerim;Park, Kyuyon;Kim, Solee;Park, Jonghun
최예림;박규연;김소이;박종헌

  • Received : 2016.01.12
  • Accepted : 2016.02.15
  • Published : 2016.02.28

Abstract

As respecting one's privacy becomes an important issue in mobile device data analysis, on-device analysis is getting attention, in which the data analysis is conducted inside a mobile device without sending data from the device to outside. One possible application of the on-device analysis is gender prediction using text data in mobile devices, such as text messages, search keyword, website bookmarks, and contact, which are highly private, and the limited computing power of mobile devices can be addressed by utilizing the word comparison method, where words are selected beforehand and delivered to a mobile device of a user to determine the user's gender by matching mobile text data and the selected words. Moreover, it is known that performing prediction after filtering instances using definite evidences increases accuracy and reduces computational complexity. In this regard, we propose a two-phase approach to on-device gender prediction, where both discriminability and popularity of a word are sequentially considered. The proposed method performs predictions using a few highly discriminative words for all instances and popular words for unclassified instances from the previous prediction. From the experiments conducted on real-world dataset, the proposed method outperformed the compared methods.

Keywords

On-Device Analysis;Gender Prediction;Mobile Text;Two Phase Approach;Discriminative Wordset;Popular Wordset

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Acknowledgement

Supported by : 한국연구재단