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
会议名称ACM Multimedia
会议录名称ACM Multimedia
期号1
页码1895-1900
会议日期2017
会议地点Mountain View, California, United States
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.
关键词Popularity Prediction Social Media Image Flickr
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/17722
专题模式识别国家重点实验室_多媒体计算与图形学
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Xiaowen Huang,Yuqi Gao,Quan Fang,et al. Towards SMP Challenge: Stacking of Diverse Models for Social Image Popularity Prediction[C],2017:1895-1900.
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