Estimating user response rate using locality sensitive hashing in search marketing

Maryam Almasharawi () and Ahmet Bulut ()
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Maryam Almasharawi: Marmara University
Ahmet Bulut: Carbon Health

Electronic Commerce Research, 2022, vol. 22, issue 1, No 3, 37-51

Abstract: Abstract Advertising to search engine users is a primary medium of online advertising. It is the largest source of revenue for search engines. Performance-driven advertising is essential for advertisers and search engines alike. The user response rate in search advertising refers to the observed rate of a desired user action such as click-through or conversion. To estimate the response rate, we built a near-neighbor based data extrapolation method called RespRate-LSH using locality sensitive hashing (LSH). The target response rate is estimated as the weighted average of the response rates of near neighbors identified via LSH. The hyper-parameters of RespRate-LSH were studied in detail, and its empirical performance was compared with traditional machine learning methods and with deep neural networks. RespRate-LSH showed exemplary performance.

Keywords: Search advertising; Response rate estimation; Locality sensitive hashing (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10660-021-09472-1

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