Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
Semantic taxonomy induction from heterogenous evidence
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Mixtures of hierarchical topics with Pachinko allocation
Proceedings of the 24th international conference on Machine learning
Automatically refining the wikipedia infobox ontology
Proceedings of the 17th international conference on World Wide Web
Building Enterprise Taxonomies
Building Enterprise Taxonomies
Deriving a large scale taxonomy from Wikipedia
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies
Journal of the ACM (JACM)
A hierarchical model of web summaries
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Non-Parametric Estimation of Topic Hierarchies from Texts with Hierarchical Dirichlet Processes
The Journal of Machine Learning Research
A graph-based algorithm for inducing lexical taxonomies from scratch
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Automatic taxonomy construction from keywords
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Taxonomy induction using hierarchical random graphs
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
SSHLDA: a semi-supervised hierarchical topic model
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Predicting primary categories of business listings for local search
Proceedings of the 21st ACM international conference on Information and knowledge management
A phrase mining framework for recursive construction of a topical hierarchy
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Emerging trends and products pose a challenge to modern search engines since they must adapt to the constantly changing needs and interests of users. For example, vertical search engines, such as Amazon, eBay, Walmart, Yelp and Yahoo! Local, provide business category hierarchies for people to navigate through millions of business listings. The category information also provides important ranking features that can be used to improve search experience. However, category hierarchies are often manually crafted by some human experts and they are far from complete. Manually constructed category hierarchies cannot handle the ever-changing and sometimes long-tail user information needs. In this paper, we study the problem of how to expand an existing category hierarchy for a search/navigation system to accommodate the information needs of users more comprehensively. We propose a general framework for this task, which has three steps: 1) detecting meaningful missing categories; 2) modeling the category hierarchy using a hierarchical Dirichlet model and predicting the optimal tree structure according to the model; 3) reorganizing the corpus using the complete category structure, i.e., associating each webpage with the relevant categories from the complete category hierarchy. Experimental results demonstrate that our proposed framework generates a high-quality category hierarchy and significantly boosts the retrieval performance.