Name
Abstract | ||
|---|---|---|
"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.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3343031.3356084 | Proceedings of the 27th ACM International Conference on Multimedia |
Keywords | Field | DocType |
popularity prediction, social multimedia, user behavior, visual prediction | Social media,Computer science,Multimedia | Conference |
ISBN | Citations | PageRank |
978-1-4503-6889-6 | 2 | 0.37 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo Wu | 1 | 58 | 8.13 |
| Wen-huang Cheng | 2 | 715 | 73.78 |
| Peiye Liu | 3 | 4 | 2.42 |
| Bei Liu | 4 | 26 | 12.94 |
| Zhaoyang Zeng | 5 | 2 | 1.38 |
| Jiebo Luo | 6 | 6314 | 374.00 |