Proceedings ArticleDOI
Topickr: flickr groups and users reloaded
Radu Andrei Negoescu,Daniel Gatica-Perez +1 more
- pp 857-860
TLDR
This work presents a topic-based approach to represent Flickr users and groups and demonstrates it with a web application, Topickr, that allows similarity based exploration of Flickr entities using their topic- based representation, learned in an unsupervised manner.Abstract:
With the increased presence of digital imaging devices there also came an explosion in the amount of multimedia content available online. Users have transformed from passive consumers of media into content creators. Flickr.com is such an example of an online community, with over 2 billion photos (and more recently, videos as well), most of which are publicly available. The user interaction with the system also provides a plethora of metadata associated with this content, and in particular tags. One very important aspect in Flickr is the ability of users to organize in self-managed communities called groups. Although users and groups are conceptually different, in practice they can be represented in the same way: a bag-of-tags, which is amenable for probabilistic topic modeling. We present a topic-based approach to represent Flickr users and groups and demonstrate it with a web application, Topickr, that allows similarity based exploration of Flickr entities using their topic-based representation, learned in an unsupervised manner.read more
Citations
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Journal ArticleDOI
Which photo groups should I choose? A comparative study of recommendation algorithms in Flickr
TL;DR: This paper designs and compares seven Flickr group recommendation models and suggests that model-based approaches are beneficial compared with memory-based approach in terms of top-k recommendation metric, and incorporating tags in the recommendation algorithms leads to an improvement of precision on the top 2% performance.
Proceedings ArticleDOI
Modeling social strength in social media community via kernel-based learning
TL;DR: A kernel-based learning to rank framework for inferring the social strength of Flickr users, which involves two learning stages and is able to conduct collaborative recommendation and collective classification.
Journal ArticleDOI
Recommending Flickr groups with social topic model
TL;DR: This paper presents a probabilistic latent topic model to model Flickr groups in an integrated framework, expecting to jointly discover the latent interests for users and groups and simultaneously learn the recommendation function.
Proceedings ArticleDOI
Flickr hypergroups
TL;DR: A novel approach to group searching through hypergroup discovery is proposed, starting from roughly 11,000 Flickr groups' content and membership information, and it is shown that hypergroups found are generally consistent and can be described through topic-based and similarity-based measures.
Proceedings ArticleDOI
Flickr group recommendation based on tensor decomposition
TL;DR: A tensor decomposition-based group recommendation model to suggest groups to users which can help tackle the problem of huge volume of groups in Flickr.
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