Modern Information Retrieval
The Journal of Machine Learning Research
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Using annotations in enterprise search
Proceedings of the 15th international conference on World Wide Web
HT06, tagging paper, taxonomy, Flickr, academic article, to read
Proceedings of the seventeenth conference on Hypertext and hypermedia
Toward bridging the annotation-retrieval gap in image search by a generative modeling approach
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
The Long Tail: Why the Future of Business Is Selling Less of More
The Long Tail: Why the Future of Business Is Selling Less of More
Optimizing web search using social annotations
Proceedings of the 16th international conference on World Wide Web
P-TAG: large scale automatic generation of personalized annotation tags for the web
Proceedings of the 16th international conference on World Wide Web
Unsupervised prediction of citation influences
Proceedings of the 24th international conference on Machine learning
Can social bookmarking improve web search?
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Designing games with a purpose
Communications of the ACM - Designing games with a purpose
Real-time automatic tag recommendation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Joint latent topic models for text and citations
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
The slashdot zoo: mining a social network with negative edges
Proceedings of the 18th international conference on World wide web
Efficient methods for topic model inference on streaming document collections
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Neighborhood-Based Tag Prediction
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
Tag refinement by regularized LDA
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Latent dirichlet allocation for tag recommendation
Proceedings of the third ACM conference on Recommender systems
Anatomy of the long tail: ordinary people with extraordinary tastes
Proceedings of the third ACM international conference on Web search and data mining
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Interactive recommendations in social endorsement networks
Proceedings of the fourth ACM conference on Recommender systems
Comment-based multi-view clustering of web 2.0 items
Proceedings of the 23rd international conference on World wide web
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Entities on social systems, such as users on Twitter, and images on Flickr, are at the core of many interesting applications: they can be ranked in search results, recommended to users, or used in contextual advertising. Such applications assume knowledge of an entity's nature and characteristic attributes. An effective way to encode such knowledge is in the form of tags. An untagged entity is practically inaccessible, since it is hard to retrieve or interact with. To address this, some platforms allow users to manually tag entities. However,while such tags can be informative, they can oftentimes be inadequate, trivial, ambiguous, or even plain false. Numerous automated tagging methods have been proposed to address these issues. However,most of them require pre-existing high-quality tags or descriptive texts for every entity that needs to be tagged. In our work, we propose a method based on social endorsements that is free from such constraints. Virtually every major social networking platform allows users to endorse entities that they find appealing. Examples include "following" Twitter users or "favoriting" Flickr photos. These endorsements are abundant and directly capture the preferences of users. In this paper, we pose and solve the problem of using the underlying social endorsement network to extract useful tags for entities in a social system. Our work leverages techniques from topic modeling to capture the interests of users and then uses them to extract relevant and descriptive tags for the entities they endorse. We perform an extensive evaluation of our proposed approach on real large-scale datasets from both Twitter and Flickr, and show that it significantly outperforms meaningful and competitive baselines.