Rethinking Relation between Model Stacking and Recurrent Neural Networks for Social Media Prediction

Chih Chung Hsu, Wen Hai Tseng, Hao Ting Yang, Chia Hsiang Lin, Chi Hung Kao

研究成果: Conference contribution

9 引文 斯高帕斯(Scopus)

摘要

Popularity prediction of social posts is one of the most critical issues for social media analysis and understanding. In this paper, we discover a more dominant feature representation of text information, as well as propose a singe ensemble learning model to obtain the popularity scores, for social media prediction challenge. However, most social media prediction techniques focus on predicting the popularity score of social posts based on a single model, such as deep learning-based or ensemble learning-based approaches. However, it is well-known that the model stacking strategy is a more effective way to boost the performance on various regression tasks. In this paper, we also show that the model stacking can be modeled as a simple recurrent neural network problem with comparable performance on predicting popularity scores. Firstly, a single strong baseline is proposed based on the deep neural network with a prediction branch. Then, the partial feature maps of the last layer of our strong baseline are used to establish a new branch with an isolated predictor. It is easy to obtain multi-prediction by repeating the above two steps. These preliminary predicted scores are then formed as the input of the recurrent unit to learn the final predicted scores, called Recurrent Stacking Model (RSM). Our experiments show that the proposed ensemble learning approach outperforms other state-of-the-art methods. Furthermore, the proposed RSM also shows the superiority over our ensemble learning approach, having verified that the model stacking problem can be transformed into the training problem of a recurrent neural network.

原文English
主出版物標題MM 2020 - Proceedings of the 28th ACM International Conference on Multimedia
發行者Association for Computing Machinery, Inc
頁面4585-4589
頁數5
ISBN(電子)9781450379885
DOIs
出版狀態Published - 2020 10月 12
事件28th ACM International Conference on Multimedia, MM 2020 - Virtual, Online, United States
持續時間: 2020 10月 122020 10月 16

出版系列

名字MM 2020 - Proceedings of the 28th ACM International Conference on Multimedia

Conference

Conference28th ACM International Conference on Multimedia, MM 2020
國家/地區United States
城市Virtual, Online
期間20-10-1220-10-16

All Science Journal Classification (ASJC) codes

  • 軟體
  • 電腦繪圖與電腦輔助設計
  • 人機介面

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