人气
一般化
计算机科学
依赖关系(UML)
社会化媒体
特征(语言学)
人工智能
特征提取
滑动窗口协议
机器学习
数据挖掘
社会性
情态动词
窗口(计算)
万维网
数学
心理学
数学分析
社会心理学
语言学
哲学
生态学
化学
高分子化学
生物
作者
Kai Wang,Peng-Hui Wang,Xin Chen,Qiushi Huang,Zhendong Mao,Yongdong Zhang
标识
DOI:10.1145/3394171.3416294
摘要
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.
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