Estimating user response rate using locality sensitive hashing in search marketing

Almasharawi M., Bulut A.

ELECTRONIC COMMERCE RESEARCH, vol.22, no.1, pp.37-51, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 22 Issue: 1
  • Publication Date: 2022
  • Doi Number: 10.1007/s10660-021-09472-1
  • Journal Name: ELECTRONIC COMMERCE RESEARCH
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, IBZ Online, ABI/INFORM, Business Source Elite, Business Source Premier, INSPEC, zbMATH
  • Page Numbers: pp.37-51
  • Keywords: Search advertising, Response rate estimation, Locality sensitive hashing
  • Acibadem Mehmet Ali Aydinlar University Affiliated: No

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.