DocumentCode :
2864393
Title :
Adaptive product normalization: using online learning for record linkage in comparison shopping
Author :
Bilenko, Mikhail ; Basil, S. ; Sahami, Mehran
Author_Institution :
Dept. of Comput. Sci., Texas Univ., Austin, TX, USA
fYear :
2005
fDate :
27-30 Nov. 2005
Abstract :
The problem of record linkage focuses on determining whether two object descriptions refer to the same underlying entity. Addressing this problem effectively has many practical applications, e.g., elimination of duplicate records in databases and citation matching for scholarly articles. In this paper, we consider a new domain where the record linkage problem is manifested: Internet comparison shopping. We address the resulting linkage setting that requires learning a similarity function between record pairs from streaming data. The learned similarity function is subsequently used in clustering to determine which records are co-referent and should be linked. We present an online machine learning method for addressing this problem, where a composite similarity function based on a linear combination of basis functions is learned incrementally. We illustrate the efficacy of this approach on several real-world datasets from an Internet comparison shopping site, and show that our method is able to effectively learn various distance functions for product data with differing characteristics. We also provide experimental results that show the importance of considering multiple performance measures in record linkage evaluation.
Keywords :
Internet; home shopping; learning (artificial intelligence); Internet comparison shopping; adaptive product normalization; online learning; online machine learning; record linkage; similarity function; Application software; Cleaning; Couplings; Data mining; Databases; Displays; Internet; Learning systems; Natural language processing; Search engines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining, Fifth IEEE International Conference on
ISSN :
1550-4786
Print_ISBN :
0-7695-2278-5
Type :
conf
DOI :
10.1109/ICDM.2005.18
Filename :
1565662
Link To Document :
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