Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Manifold-ranking based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Smooth minimization of non-smooth functions
Mathematical Programming: Series A and B
Higher order learning with graphs
ICML '06 Proceedings of the 23rd international conference on Machine learning
Why we tag: motivations for annotation in mobile and online media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Video search re-ranking via multi-graph propagation
Proceedings of the 15th international conference on Multimedia
Video search reranking through random walk over document-level context graph
Proceedings of the 15th international conference on Multimedia
Harvesting with SONAR: the value of aggregating social network information
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Learning multiple graphs for document recommendations
Proceedings of the 17th international conference on World Wide Web
Proceedings of the 18th international conference on World wide web
Personalized tag recommendation using graph-based ranking on multi-type interrelated objects
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Social networks and discovery in the enterprise (SaND)
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Mining heterogeneous information networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Unified tag analysis with multi-edge graph
Proceedings of the international conference on Multimedia
Music recommendation by unified hypergraph: combining social media information and music content
Proceedings of the international conference on Multimedia
Efficient object category recognition using classemes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Ranking-based classification of heterogeneous information networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast image/video collection summarization with local clustering
Proceedings of the 21st ACM international conference on Multimedia
Latent feature learning in social media network
Proceedings of the 21st ACM international conference on Multimedia
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Analysis and recommendation of multimedia information can be greatly improved if we know the interactions between the content, user, and concept, which can be easily observed from the social media networks. However, there are many heterogeneous entities and relations in such networks, making it difficult to fully represent and exploit the diverse array of information. In this paper, we develop a hybrid social media network, through which the heterogeneous entities and relations are seamlessly integrated and a joint inference procedure across the heterogeneous entities and relations can be developed. The network can be used to generate personalized information recommendation in response to specific targets of interests, e.g., personalized multimedia albums, target advertisement and friend/topic recommendation. In the proposed network, each node denotes an entity and the multiple edges between nodes characterize the diverse relations between the entities (e.g., friends, similar contents, related concepts, favorites, tags, etc). Given a query from a user indicating his/her information needs, a propagation over the hybrid social media network is employed to infer the utility scores of all the entities in the network while learning the edge selection function to activate only a sparse subset of relevant edges, such that the query information can be best propagated along the activated paths. Driven by the intuition that much redundancy exists among the diverse relations, we have developed a robust optimization framework based on several sparsity principles. We show significant performance gains of the proposed method over the state of the art in multimedia retrieval and recommendation using data crawled from social media sites. To the best of our knowledge, this is the first model supporting not only aggregation but also judicious selection of heterogeneous relations in the social media networks.