Towards SMP Challenge: Stacking of Diverse Models for Social Image Popularity Prediction

Popularity prediction on social media has attracted extensive attention nowadays due to its widespread applications, such as online marketing and economical trends. In this paper, we describe a solution of our team CASIA-NLPR-MMC for Social Media Prediction (SMP) challenge. This challenge is designed to predict the popularity of social media posts. We present a stacking framework by combining a diverse set of models to predict the popularity of images on Flickr using user-centered, image content and image context features. Several individual models are employed for scoring popularity of an image at earlier stage, and then a stacking model of Support Vector Regression (SVR) is utilized to train a meta model of different individual models trained beforehand. The Spearman's Rho of this Stacking model is 0.88 and the mean absolute error is about 0.75 on our test set. On the official final-released test set, the Spearman's Rho is 0.7927 and mean absolute error is about 1.1783. The results on provided dataset demonstrate the effectiveness of our proposed approach for image popularity prediction.