World Wide Web https://doi.org/10.1007/s11280-018-0590-1 A layer-wise deep stacking model for social image popularity prediction 1 1 1 Zehang Lin · Feitao Huang · Yukun Li · 1 1 Zhenguo Yang · Wenyin Liu Received: 30 November 2017 / Revised: 26 March 2018 / Accepted: 17 May 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 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. Keywords Social media analysis · Social image popularity
World Wide Web – Springer Journals
Published: May 28, 2018
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