Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Fast Radial Symmetry for Detecting Points of Interest
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Using phase information for symmetry detection
Pattern Recognition Letters
Phase congruence measurement for image similarity assessment
Pattern Recognition Letters
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Personalized, interactive tag recommendation for flickr
Proceedings of the 2008 ACM conference on Recommender systems
SheepDog: group and tag recommendation for flickr photos by automatic search-based learning
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Kodak moments and Flickr diamonds: how users shape large-scale media
Proceedings of the international conference on Multimedia
Which photo groups should I choose? A comparative study of recommendation algorithms in Flickr
Journal of Information Science
A3P: adaptive policy prediction for shared images over popular content sharing sites
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Scalable Affiliation Recommendation using Auxiliary Networks
ACM Transactions on Intelligent Systems and Technology (TIST)
Why do we converse on social media?: an analysis of intrinsic and extrinsic network factors
WSM '11 Proceedings of the 3rd ACM SIGMM international workshop on Social media
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In this paper we develop a recommendation framework to connect image content with communities in online social media. The problem is important because users are looking for useful feedback on their uploaded content, but finding the right community for feedback is challenging for the end user. Social media are characterized by both content and community. Hence, in our approach, we characterize images through three types of features: visual features, user generated text tags, and social interaction (user communication history in the form of comments). A recommendation framework based on learning a latent space representation of the groups is developed to recommend the most likely groups for a given image. The model was tested on a large corpus of Flickr images comprising 15, 689 images. Our method outperforms the baseline method, with a mean precision 0.62 and mean recall 0.69. Importantly, we show that fusing image content, text tags with social interaction features outperforms the case of only using image content or tags.