"SMP Challenge" aims to discover novel prediction tasks for numerous data on
social multimedia and seek excellent research teams. Making predictions via
social multimedia data (e.g. photos, videos or news) is not only helps us to
make better strategic decisions for the future, but also explores advanced
predictive learning and analytic methods on various problems and scenarios,
such as multimedia recommendation, advertising system, fashion analysis etc.
In the SMP Challenge at ACM Multimedia 2019, we introduce a novel prediction
task Temporal Popularity Prediction, which focuses on predicting future
interaction or attractiveness (in terms of clicks, views or likes etc.) of new
online posts in social media feeds before uploading. We also collected and
released a large-scale SMPD benchmark with over 480K posts from 69K users. In
this paper, we define the challenge problem, give an overview of the dataset,
present statistics of rich information for data and annotation and design the
accuracy and correlation evaluation metrics for temporal popularity prediction
to the challenge.

本文介绍了 “SMP Challenge” 中的新预测任务 ——“时序流行度预测”，该任务旨在利用社交多媒体数据进行未来互动性或吸引力的预测，并发布了一个包含超过 480K 发布内容和 69K 用户的大型 SMPD 基准数据集。