The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
A systematic comparison of various statistical alignment models
Computational Linguistics
The Journal of Machine Learning Research
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Blogosonomy: Autotagging Any Text Using Bloggers' Knowledge
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Introduction to Information Retrieval
Introduction to Information Retrieval
Tag recommendations in social bookmarking systems
AI Communications
Learning optimal ranking with tensor factorization for tag recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Latent dirichlet allocation for tag recommendation
Proceedings of the third ACM conference on Recommender systems
Modern Information Retrieval
Content-based recommendation in social tagging systems
Proceedings of the fourth ACM conference on Recommender systems
Semantic tags generation and retrieval for online advertising
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Recommender Systems Handbook
A simple word trigger method for social tag suggestion
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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Social tagging systems, which allow users to freely annotate online resources with tags, become popular in the Web 2.0 era. In order to ease the annotation process, research on social tag recommendation has drawn much attention in recent years. Modeling the social tagging behavior could better reflect the nature of this issue and improve the result of recommendation. In this paper, we proposed a novel approach for bringing the associative ability to model the social tagging behavior and then to enhance the performance of automatic tag recommendation. To simulate human tagging process, our approach ranks the candidate tags on a weighted digraph built by the semantic relationships among meaningful words in the summary and the corresponding tags for a given resource. The semantic relationships are learnt via a word alignment model in statistical machine translation on large datasets. Experiments on real world datasets demonstrate that our method is effective, robust and language-independent compared with the state-of-the-art methods.