NDLI: Predicting primary categories of business listings for local search

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Predicting primary categories of business listings for local search

Content Provider	ACM Digital Library
Author	Kang, Changsung Chang, Yi Lee, Jeehaeng
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.
Starting Page	2591
Ending Page	2594
Page Count	4
File Format	PDF
ISBN	9781450311564
DOI	10.1145/2396761.2398699
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-10-29
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Vertical search Text categorization Primary category
Content Type	Text
Resource Type	Article

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