An iterative refinement approach for social media headline prediction

Chih Chung Hsu, Jun Yi Lee, Jing Wen Lin, Chia Yen Lee, Tsai Yne Hou, Ching Yi Hsueh, Hsiang Chin Chien, Ting Xuan Liao, Ying Chu Kuo, Zhong Xuan Zhang

研究成果: Conference contribution

12 引文 斯高帕斯(Scopus)

摘要

In this study, we propose a novel iterative refinement approach to predict the popularity score of the social media meta-data effectively. With the rapid growth of the social media on the Internet, how to adequately forecast the view count or popularity becomes more important. Conventionally, the ensemble approach such as random forest regression achieves high and stable performance on various prediction tasks. However, most of the regression methods may not precisely predict the extreme high or low values. To address this issue, we first predict the initial popularity score and retrieve their residues. In order to correctly compensate those extreme values, we adopt an ensemble regressor to compensate the residues to further improve the prediction performance. Comprehensive experiments are conducted to demonstrate the proposed iterative refinement approach outperforms the state-of-the-art regression approach.

原文English
主出版物標題MM 2018 - Proceedings of the 2018 ACM Multimedia Conference
發行者Association for Computing Machinery, Inc
頁面2008-2012
頁數5
ISBN(電子)9781450356657
DOIs
出版狀態Published - 2018 10月 15
事件26th ACM Multimedia conference, MM 2018 - Seoul, Korea, Republic of
持續時間: 2018 10月 222018 10月 26

出版系列

名字MM 2018 - Proceedings of the 2018 ACM Multimedia Conference

Other

Other26th ACM Multimedia conference, MM 2018
國家/地區Korea, Republic of
城市Seoul
期間18-10-2218-10-26

All Science Journal Classification (ASJC) codes

  • 電腦繪圖與電腦輔助設計
  • 人機介面

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