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An introduction to support Vector Machines: and other kernel-based learning methods
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The complex dynamics of collaborative tagging
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Topic-link LDA: joint models of topic and author community
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Latent dirichlet allocation for tag recommendation
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Learning author-topic models from text corpora
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Multiplicative latent factor models for description and prediction of social networks
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Folks in Folksonomies: social link prediction from shared metadata
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The topic-perspective model for social tagging systems
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Survey on social tagging techniques
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Investigating topic models for social media user recommendation
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Predicting friendship links in social networks using a topic modeling approach
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Link prediction via matrix factorization
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A generalized stochastic block model for recommendation in social rating networks
Proceedings of the fifth ACM conference on Recommender systems
Second workshop on information heterogeneity and fusion in recommender systems (HetRec2011)
Proceedings of the fifth ACM conference on Recommender systems
The dynamic features of delicious, flickr, and YouTube
Journal of the American Society for Information Science and Technology
Context-aware music recommendation based on latenttopic sequential patterns
Proceedings of the sixth ACM conference on Recommender systems
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As social media sites grow in popularity, tagging has naturally emerged as a method of searching, categorizing and filtering online information, especially multimedia content. The unrestricted vocabulary users choose from to annotate content however, has often lead to an explosion of the size of space in which search is performed. This paper is concerned with investigating generative models of social annotations, and testing their efficiency with respect to two information consumption oriented tasks. One task considers recommending new tags (similarly new resources) for new, previously unknown users. We use perplexity as a standard measure for estimating the generalization performance of a probabilistic model. The second task is aimed at recommending new users to connect with. In this task, we examine which users' activity is most discriminative in predicting social ties: annotation (i.e. tags), resource usage (i.e. artists), or collective annotation of resources altogether. For the second task, we propose a framework to integrate the modeling of social annotations with network proximity. The proposed approach consists of two steps: (1) discovering salient topics that characterize users, resources and annotations; and (2) enhancing the recommendation power of such models by incorporating social clues from the immediate neighborhood of users. In particular, we propose four classification schemes for social link recommendation, which we evaluate on a real--world dataset from Last.fm. Our results demonstrate significant improvements over traditional approaches.