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

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 多媒体计算与图形学团队

	Towards SMP Challenge: Stacking of Diverse Models for Social Image Popularity Prediction
	Xiaowen Huang; Yuqi Gao; Quan Fang; Sang JT(桑基韬); Changsheng Xu
	2017
会议日期	2017
会议地点	Mountain View, California, United States
关键词	Popularity Prediction Social Media Image Flickr
期号	1
页码	1895-1900
英文摘要	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.
会议录	ACM Multimedia
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/17722]
专题	自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
推荐引用方式 GB/T 7714	Xiaowen Huang,Yuqi Gao,Quan Fang,et al. Towards SMP Challenge: Stacking of Diverse Models for Social Image Popularity Prediction[C]. 见:. Mountain View, California, United States. 2017.