A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
An Introduction to the Conjugate Gradient Method Without the Agonizing Pain
An Introduction to the Conjugate Gradient Method Without the Agonizing Pain
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
PageRank without hyperlinks: structural re-ranking using links induced by language models
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Regularizing ad hoc retrieval scores
Proceedings of the 14th ACM international conference on Information and knowledge management
Respect my authority!: HITS without hyperlinks, utilizing cluster-based language models
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
The complex dynamics of collaborative tagging
Proceedings of the 16th international conference on World Wide Web
Optimizing web search using social annotations
Proceedings of the 16th international conference on World Wide Web
Combining content and link for classification using matrix factorization
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A tutorial on spectral clustering
Statistics and Computing
Can social bookmarking improve web search?
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Learning to rank relational objects and its application to web search
Proceedings of the 17th international conference on World Wide Web
Tag-based social interest discovery
Proceedings of the 17th international conference on World Wide Web
Exploring social annotations for information retrieval
Proceedings of the 17th international conference on World Wide Web
A Unified View of Matrix Factorization Models
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Towards a theoretical foundation for Laplacian-based manifold methods
Journal of Computer and System Sciences
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Effective latent space graph-based re-ranking model with global consistency
Proceedings of the Second ACM International Conference on Web Search and Data Mining
A statistical comparison of tag and query logs
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Semi-supervised learning with very few labeled training examples
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Relation regularized matrix factorization
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Social media recommendation based on people and tags
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Exploring categorization property of social annotations for information retrieval
Proceedings of the 20th ACM international conference on Information and knowledge management
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Leveraging Social Bookmarks from Partially Tagged Corpus for Improved Web Page Clustering
ACM Transactions on Intelligent Systems and Technology (TIST)
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Social annotations provide additional document description contributed by online users and they have been explored for improving search performance. However, most existing methods need offline analysis of the whole tagged corpus, which is computationally expensive and cannot fit specific queries well. In this paper, we propose to use tags for document re-ranking. Specifically, we first estimate document similarity by combining words and tags and then adjust the document ranks with the assumption that similar documents should have similar retrieval scores. On similarity estimation, we present a new feature extraction method, called CRMF, from which document similarity can be derived. The CRMF can integrate the content and relation properties of multiple views and mine their correspondence. Besides, it does not require that all the documents to have tags. We tested the proposed approach on collections which are derived from Clue Web and contain Delicious tags. The experimental results demonstrate the effectiveness of tags on document re-ranking, where CRMF is significantly better than other state-of-the-art methods using tags.