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dc.contributor.authorWu, Boen_US
dc.contributor.authorCheng, Wen-Huangen_US
dc.contributor.authorLiu, Peiyeen_US
dc.contributor.authorLiu, Beien_US
dc.contributor.authorZeng, Zhaoyangen_US
dc.contributor.authorLuo, Jieboen_US
dc.date.accessioned2020-03-02T03:23:53Z-
dc.date.available2020-03-02T03:23:53Z-
dc.date.issued2019-01-01en_US
dc.identifier.isbn978-1-4503-6889-6en_US
dc.identifier.urihttp://dx.doi.org/10.1145/3343031.3356084en_US
dc.identifier.urihttp://hdl.handle.net/11536/153833-
dc.description.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.en_US
dc.language.isoen_USen_US
dc.subjectSocial Multimediaen_US
dc.subjectVisual Predictionen_US
dc.subjectPopularity Predictionen_US
dc.titleSMP Challenge: An Overview of Social Media Prediction Challenge 2019en_US
dc.typeProceedings Paperen_US
dc.identifier.doi10.1145/3343031.3356084en_US
dc.identifier.journalPROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA (MM'19)en_US
dc.citation.spage2667en_US
dc.citation.epage2671en_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.identifier.wosnumberWOS:000509743400348en_US
dc.citation.woscount0en_US
Appears in Collections:Conferences Paper