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Predicting primary categories of business listings for local search

Authors:

Changsung Kang,

Jeehaeng Lee,

Yi ChangAuthors Info & Claims

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

Pages 2591 - 2594

https://doi.org/10.1145/2396761.2398699

Published: 29 October 2012 Publication History

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Abstract

We consider the problem of identifying primary categories of a business listing among the categories provided by the owner of the business. The category information submitted by business owners cannot be trusted with absolute certainty since they may purposefully add some secondary or irrelevant categories to increase recall in local search results, which makes category search very challenging for local search engines. Thus, identifying primary categories of a business is a crucial problem in local search. This problem can be cast as a multi-label classification problem with a large number of categories. However, the large scale of the problem makes it infeasible to use conventional supervised-learning-based text categorization approaches.

We propose a large-scale classification framework that leverages multiple types of classification labels to produce a highly accurate classifier with fast training time. We effectively combine the complementary label sources to refine prediction. The experimental results indicate that our framework achieves very high precision and recall and outperforms a Centroid-based method.

References

[1]

W. W. Cohen and Y. Singer. Context-sensitive learning methods for text categorization. ACM Trans. Inf. Syst., 17(2):141--173, Apr. 1999.

Digital Library

Google Scholar

[2]

J. Friedman. Greedy function approximation: a gradient boosting machine. Ann. Statist., 29:1189--1232, 2001.

Crossref

Google Scholar

[3]

T. Joachims. A probabilistic analysis of the rocchio algorithm with tfidf for text categorization. In Proceedings of the Fourteenth International Conference on Machine Learning, ICML '97, pages 143--151, San Francisco, CA, USA, 1997. Morgan Kaufmann Publishers Inc.

Digital Library

Google Scholar

[4]

T. Joachims. Text categorization with suport vector machines: Learning with many relevant features. In Proceedings of the 10th European Conference on Machine Learning, ECML '98, pages 137--142, London, UK, UK, 1998. Springer-Verlag.

Digital Library

Google Scholar

[5]

T. Joachims, Y. Yang. Text categorization. Scholarpedia, 3(5):4242, 2008.

Crossref

Google Scholar

[6]

C. Kang, X. Wang, Y. Chang, and B. Tseng. Learning to rank with multi-aspect relevance for vertical search. In Proceedings of the fifth ACM international conference on Web search and data mining, WSDM '12, pages 453--462, New York, NY, USA, 2012. ACM.

Digital Library

Google Scholar

[7]

F. Sebastiani. Machine learning in automated text categorization. ACM Comput. Surv., 34(1):1--47, Mar. 2002.

Digital Library

Google Scholar

[8]

F. Sebastiani. Text categorization. In Text Mining and its Applications to Intelligence, CRM and Knowledge Management, pages 109--129. WIT Press, 2005.

Google Scholar

[9]

P. Venetis, H. Gonzalez, C. S. Jensen, and A. Y. Halevy. Hyper-local, directions-based ranking of places. PVLDB, 4(5):290--301, 2011.

Digital Library

Google Scholar

[10]

Y. Yang and J. O. Pedersen. A comparative study on feature selection in text categorization. pages 412--420. Morgan Kaufmann Publishers, 1997.

Digital Library

Google Scholar

Cited By

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Wang JKang CChang YHan JChung CBroder AShim KSuel T(2014)A hierarchical Dirichlet model for taxonomy expansion for search enginesProceedings of the 23rd international conference on World wide web10.1145/2566486.2568037(961-970)Online publication date: 7-Apr-2014
Long BChang Y(2014)Relevance Ranking for Vertical Search EnginesundefinedOnline publication date: 14-Feb-2014

Index Terms

Predicting primary categories of business listings for local search
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Information & Contributors

Information

Published In

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

October 2012

2840 pages

ISBN:9781450311564

DOI:10.1145/2396761

General Chair:
Xuewen Chen
Wayne State University, USA
,
Program Chairs:
Guy Lebanon
Georgia Institute of Technology
,
Haixun Wang
Microsoft Research Asia
,
Mohammed J. Zaki
Rensselaer Polytechnic Institute

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2012

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CIKM'12: 21st ACM International Conference on Information and Knowledge Management

October 29 - November 2, 2012

Hawaii, Maui, USA

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Wang JKang CChang YHan JChung CBroder AShim KSuel T(2014)A hierarchical Dirichlet model for taxonomy expansion for search enginesProceedings of the 23rd international conference on World wide web10.1145/2566486.2568037(961-970)Online publication date: 7-Apr-2014
Long BChang Y(2014)Relevance Ranking for Vertical Search EnginesundefinedOnline publication date: 14-Feb-2014

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Predicting primary categories of business listings for local search ranking

Hierarchical directory mapping for category-constrained meta-search

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