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.
World Wide Web – Springer Journals
Published: May 28, 2018
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera