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Electronic Commerce Research

Erschienen in:

28.03.2021

Estimating user response rate using locality sensitive hashing in search marketing

verfasst von: Maryam Almasharawi, Ahmet Bulut

Erschienen in: Electronic Commerce Research | Ausgabe 1/2022

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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.

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Titel: Estimating user response rate using locality sensitive hashing in search marketing
verfasst von: Maryam Almasharawi
Ahmet Bulut
Publikationsdatum: 28.03.2021
Verlag: Springer US
Erschienen in: Electronic Commerce Research / Ausgabe 1/2022
Print ISSN: 1389-5753
Elektronische ISSN: 1572-9362
DOI: https://doi.org/10.1007/s10660-021-09472-1

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