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The Ambiguous Identifier Clustering Technique

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Abstract

Investigations of online transaction data often face the problem that entries for identical products cannot be identified as such. There is, for example, typically no unique product identifier in online auctions; retailers make their offers at price comparison sites hardly comparable and online stores often use different identifiers for virtually equal products. Existing studies typically use data sets that are restricted to one or only a few products in order to avoid product heterogeneity if a unique product identifier is not available. We propose the Ambiguous Identifier Clustering Technique (AICT) that identifies online transaction data that refer to virtually the same product. Based on a data set of eBay auctions, we demonstrate that AICT clusters online transactions for identical products with high accuracy. We further show how researchers benefit from AICT and the reduced product heterogeneity when analyzing data with econometric models.

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Notes

  1. An n-gram is a successional subsequence of n items (characters or word) of a text. The character-based n-grams of size 2 (bi-grams) of the word “text” are “te”, “ex”, “xt”. A detailed example of character-based n-grams can be found in Table 1.

  2. An overview of textual features is presented in Abbasi and Chen 2008 and in Zheng et al. 2006.

  3. We use a very short stop word list containing only one entry for each year between 1999 and 2013, and the following words: album, cd, digipack, dvd, edition, emi, hardback, hardcover, limited, paperback.

  4. We found 942 different bi-grams in our data set.

  5. We used conditional inference trees (see Hothorn et al. 2006 for a detailed description of this method) with 10,000 Monte Carlo replications to test for significant splits of ending time.

  6. PriceGrabber.com, for example, lists more than 120 products when searching for “canon powershots × 50 hs”. Some of these products are colored variants of the camera, some are bundles including the camera and some are supplements such as bags or tripods.

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  1. University of Passau, Innstr. 43, 94032, Passau, Germany

    Michael Scholz, Markus Franz & Oliver Hinz

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  1. Michael Scholz
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  2. Markus Franz
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  3. Oliver Hinz
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Correspondence to Michael Scholz.

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Scholz, M., Franz, M. & Hinz, O. The Ambiguous Identifier Clustering Technique. Electron Markets 26, 143–156 (2016). https://doi.org/10.1007/s12525-016-0217-2

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