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World Wide Web
Erschienen in: World Wide Web 4/2019

28.05.2018

A layer-wise deep stacking model for social image popularity prediction

verfasst von: Zehang Lin, Feitao Huang, Yukun Li, Zhenguo Yang, Wenyin Liu

Erschienen in: World Wide Web | Ausgabe 4/2019

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Abstract

In this paper, we present a Layer-wise Deep Stacking (LDS) model to predict the popularity of Flickr-like social posts. LDS stacks multiple regression models in multiple layers, which enables the different models to complement and reinforce each other. To avoid overfitting, a dropout module is introduced to randomly activate the data being fed into the regression models in each layer. In particular, a detector is devised to determine the depth of LDS automatically by monitoring the performance of the features achieved by the LDS layers. Extensive experiments conducted on a public dataset consisting of 432K Flickr image posts manifest the effectiveness and significance of the LDS model and its components. LDS achieves competitive performance on multiple metrics: Spearman’s Rho: 83.50%, MAE: 1.038, and MSE: 2.011, outperforming state-of-the-art approaches for social image popularity prediction.
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Metadaten
Titel
A layer-wise deep stacking model for social image popularity prediction
verfasst von
Zehang Lin
Feitao Huang
Yukun Li
Zhenguo Yang
Wenyin Liu
Publikationsdatum
28.05.2018
Verlag
Springer US
Erschienen in
World Wide Web / Ausgabe 4/2019
Print ISSN: 1386-145X
Elektronische ISSN: 1573-1413
DOI
https://doi.org/10.1007/s11280-018-0590-1

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