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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationMM 2020 - Proceedings of the 28th ACM International Conference on Multimedia
PublisherAssociation for Computing Machinery, Inc
Pages4585-4589
Number of pages5
ISBN (Electronic)9781450379885
DOIs
Publication statusPublished - 2020 Oct 12
Event28th ACM International Conference on Multimedia, MM 2020 - Virtual, Online, United States
Duration: 2020 Oct 122020 Oct 16

Publication series

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

Conference

Conference28th ACM International Conference on Multimedia, MM 2020
Country/TerritoryUnited States
CityVirtual, Online
Period20-10-1220-10-16

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction

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