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Adaptive Product Normalization: Using Online Learning for Record Linkage in Comparison Shopping

Adaptive Product Normalization: Using Online Learning for Record Linkage in Comparison Shopping,10.1109/ICDM.2005.18,Mikhail Bilenko,Sugato Basu,Mehra

Adaptive Product Normalization: Using Online Learning for Record Linkage in Comparison Shopping   (Citations: 29)
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The problem of record linkage focuses on determining whether two object descriptions refer to the same under- lying 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 shop- ping. We address the resulting linkage setting that requires learning a similarity function between record pairs from streaming data. The learned similarity function is subse- quently used in clustering to determine which records are co-referent and should be linked. We present an online ma- chine learning method for addressing this problem, where a composite similarity function based on a linear combi- nation of basis functions is learned incrementally. We il- lustrate 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 charac- teristics. We also provide experimental results that show the importance of considering multiple performance measures in record linkage evaluation.
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