- Author
- Year
- 2015
- Title
- Latent Factors of Visual Popularity Prediction
- Event
- 2015 ACM International Conference on Multimedia Retrieval
- Book/source title
- ICMR'15: proceedings of the 2015 ACM International Conference on Multimedia Retrieval: June 23-26, 2015, Shanghai, China
- Pages (from-to)
- 195-202
- Publisher
- New York, NY: Association for Computing Machinery
- ISBN
- 9781450332743
- Document type
- Conference contribution
- Faculty
- Faculty of Science (FNWI)
- Institute
- Informatics Institute (IVI)
- Abstract
-
Predicting the popularity of an image on social networks based solely on its visual content is a difficult problem. One image may become widely distributed and repeatedly shared, while another similar image may be totally overlooked. We aim to gain insight into how visual content affects image popularity. We propose a latent ranking approach that takes into account not only the distinctive visual cues in popular images, but also those in unpopular images. This method is evaluated on two existing datasets collected from photo-sharing websites, as well as a new proposed dataset of images from the microblogging website Twitter. Our experiments investigate factors of the ranking model, the level of user engagement in scoring popularity, and whether the discovered senses are meaningful. The proposed approach yields state of the art results, and allows for insight into the semantics of image popularity on social networks.
- URL
- go to publisher's site
- Language
- English
- Persistent Identifier
- https://hdl.handle.net/11245/1.478831
- Downloads
-
2671188.2749405(Final published version)
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