"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.

中文翻译：

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SMP 挑战赛：2019 年社交媒体预测挑战赛概览

“SMP挑战”旨在为社交多媒体上的海量数据发现新颖的预测任务，并寻求优秀的研究团队。通过社交多媒体数据（例如照片、视频或新闻）进行预测不仅有助于我们为未来做出更好的战略决策，而且还探索了对各种问题和场景的先进预测学习和分析方法，例如多媒体推荐、广告系统、时尚分析等。 在 ACM Multimedia 2019 的 SMP Challenge 中，我们引入了一个新颖的预测任务 Temporal Popularity Prediction，它侧重于预测社交网络中新的在线帖子的未来互动或吸引力（在点击、观看或喜欢等方面）。上传前的媒体提要。我们还收集并发布了一个大规模 SMPD 基准测试，其中包含来自 69K 用户的超过 480K 的帖子。