A Feature Generalization Framework for Social Media Popularity Prediction
Kai Wang, Penghui Wang, Xin Chen, Qiushi Huang, Zhendong Mao, Yongdong Zhang
Abstract
Social media is an indispensable part in modern life and social media popularity prediction can be applied to many aspects of sociality. In this paper, we propose a novel combined framework for social media popularity prediction, which accomplishes feature generalization and temporal modeling based on multi-modal feature extraction. On the one hand, in order to address the generalization problem caused by massive missing data, we train two CatBoost models with different datasets and integrate their outputs with a linear combination. On the other hand, sliding window average is employed to mine potential short-term dependency for each user's post sequence. Extensive experiments show that our proposed framework has superiorities in both feature generalization and temporal modeling. Besides, our approach achieves the 1st place on the leader board of the SMP Challenge in 2020, which proves the effectiveness of our proposed framework.