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.

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In Proceedings of the 5th International Conference on Data Mining (ICDM-2005), pp. 58--65, Houston, TX, November 2005.

@inproceedings{bilenko:icdm05, title={Adaptive Product Normalization: Using Online Learning for Record Linkage in Comparison Shopping}, author={Mikhail Bilenko and Sugato Basu and Mehran Sahami}, booktitle={Proceedings of the 5th International Conference on Data Mining (ICDM-2005)}, month={November}, address={Houston, TX}, pages={58--65}, url="http://www.cs.utexas.edu/users/ai-lab?bilenko:submitted05", year={2005} }