HyFea: Winning Solution to Social Media Popularity Prediction for Multimedia Grand Challenge 2020
Xin Lai, Yihong Zhang, Wei Zhang
Abstract
Social Media Popularity (SMP) prediction focuses on predicting the social impact of a given post from a specific user in social media, which is crucial for online advertising, social recommendation, and demand prediction. In this paper, we present HyFea, our winning solution to the Social Media Prediction (SMP) Challenge for multimedia grand challenge of ACM Multimedia 2020. To address the multi-modality and personality issues of this challenge, HyFea carefully considers multiple feature types and adopts a tree-based ensembling method, i.e., CatBoost, which is shown to perform well in prediction. Specifically, HyFea involves the features related to Image, Category, Space-Time, User Profile, Tag, and Others. We conduct several experiments on the Social Media Prediction Dataset (SMPD), verifying the positive contributions of each type of features.