A language modeling approach to information retrieval
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Maximizing the spread of influence through a social network
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Human influence network for understanding leadership behavior
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Influence and correlation in social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Social influence analysis in large-scale networks
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Social influence and the diffusion of user-created content
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Personalized social search based on the user's social network
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NUS-WIDE: a real-world web image database from National University of Singapore
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Scalable influence maximization for prevalent viral marketing in large-scale social networks
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Mining topic-level influence in heterogeneous networks
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Personalized search on Flickr based on searcher's preference prediction
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Modeling social strength in social media community via kernel-based learning
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Inferring Networks of Diffusion and Influence
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Collective search and recommendation in social media
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Latent feature learning in social media network
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Social influence analysis and application on multimedia sharing websites
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special Sections on the 20th Anniversary of ACM International Conference on Multimedia, Best Papers of ACM Multimedia 2012
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Social media is becoming popular these days, where user necessarily interacts with each other to form social networks. Influence network, as one special case of social network, has been recognized as significantly impacting social activities and user decisions. We emphasize in this paper that the inter-user influence is essentially topic-sensitive, as for different tasks users tend to trust different influencers and be influenced most by them. While existing research focuses on global influence modeling and applies to text-based networks, this work investigates the problem of topic-sensitive influence modeling in the multimedia domain. We propose a multi-modal probabilistic model, considering both users' textual annotation and uploaded visual image. This model is capable of simultaneously extracting user topic distributions and topic-sensitive influence strengths. By identifying the topic-sensitive influencer, we are able to conduct applications like collective search and collaborative recommendation. A risk minimization-based general framework for personalized image search is further presented, where the image search task is transferred to measure the distance of image and personalized query language models. The framework considers the noisy tag issue and enables easy incorporation of social influence. We have conducted experiments on a large-scale Flickr dataset. Qualitative as well as quantitative evaluation results have validated the effectiveness of the topic-sensitive influencer mining model, and demonstrated the advantage of incorporating topic-sensitive influence in personalized image search and topic-based image recommendation.