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Predicting User-to-content Links in Flickr Groups

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2 Author(s)
Sumit Negi ; IBM Res., New Delhi, India ; Santanu Chaudhury

The last few years have seen an exponential increase in the amount of multimedia content that is available online thanks to collaborative-online communities such as Flickr, You Tube etc. As opposed to "pure" social networking services these collaborative-online communities not only allow users to create new social links (e.g. add other users to their friend or contact list) but also allow users to contribute multimedia content and engage in content-driven interactions (called user-to-content interactions). A good example of this can be seen in Flickr, in general and Flickr Group in particular where users can comment on or "like" an image contributed by another user. This paper looks at the task of predicting the formation of such user-to-content links in Flickr Groups. More specifically, "what is the chance that a user will comment/like an image contributed by another user?". Our proposed method for predicting user-to-content links takes into account both community effect and content effect. Our results on real-world Flickr Group data reveals that the proposed method shows good performance for the user-to-content link prediction task.

Published in:

Advances in Social Networks Analysis and Mining (ASONAM), 2012 IEEE/ACM International Conference on

Date of Conference:

26-29 Aug. 2012