"Nobody comes here anymore, it's too crowded": predicting image popularity on Flickr

Predicting popular content is a challenging problem for social media websites in order to encourage user interactions and activity. Existing works in this area, including the recommendation approach used by Flickr (called "interestingness"), consider only click through data, tags, comments and explicit user feedback in this computation. On image sharing websites, however, many images are annotated with no tags and initially, an image has no interaction data. In this case, these existing approaches fail due to lack of evidence. In this paper, we therefore focus on image popularity prediction in a cold start scenario (i.e. where there exist no, or limited, textual/interaction data), by considering an image's context, visual appearance and user context. Specifically, we predict the number of comments and views an image has based on a number of new features for this propose. Experimenting on the MIR-Flickr 1M collection, we are able to overcome the problems associated with popularity prediction in a cold start, achieving accuracy of up to 76%.