Title :
Wikipedia edit number prediction from the past edit record based on auto-supervised learning
Author :
Yoshida, Yutaka ; Ohwada, Hayato
Author_Institution :
Dept. of Ind. Adm., Tokyo Univ. of Sci., Tokyo, Japan
Abstract :
This paper describes our approach to the Wikipedia Participation Challenge that seeks to predict the number of edits a Wikipedia editor will make in the next five months. Our approach takes a time series analysis approach in combination with supervised learning, which we call auto-supervised learning. The best model of our solutions achieved a 41% improvement over WMF´s baseline predictive model. The result is low accuracy compared with related work but showed 9th place in ICDM contest.
Keywords :
Web sites; learning (artificial intelligence); time series; ICDM contest; Wikipedia edit number prediction; Wikipedia editor; Wikipedia participation challenge; auto-supervised learning; past edit record; time series analysis approach; Electronic publishing; Encyclopedias; Internet; Radio frequency; Reliability;
Conference_Titel :
Systems and Informatics (ICSAI), 2012 International Conference on
Conference_Location :
Yantai
Print_ISBN :
978-1-4673-0198-5
DOI :
10.1109/ICSAI.2012.6223541