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ROI constrained optimal online allocation in sponsored search.

Wenjian Xu1,2, Shui Liu3, Zulong Chen4

  • 1School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, China.

Scientific Reports
|October 30, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for sponsored search ad allocation to boost e-commerce revenue. The model enhances ad relevance, increasing platform earnings without compromising advertiser return on investment (ROI).

Keywords:
Advertising systemsAuction mechanismOnline allocationSponsored Search

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Area of Science:

  • E-commerce
  • Advertising Technology
  • Operations Research

Background:

  • Sponsored search is crucial for e-commerce revenue.
  • Current ad allocation methods often overlook sponsored search specifics, focusing on display ads.
  • Balancing platform revenue with user experience and advertiser needs is complex.

Purpose of the Study:

  • To develop a framework for large-scale sponsored search ad allocation.
  • To optimize real-time ad matching for maximum platform revenue.
  • To address challenges in sponsored search allocation not covered by existing research.

Main Methods:

  • An offline optimization component using a parameter-server architecture.
  • An online strategy to manage conflicts with the auction mechanism.
  • Evaluation through offline analysis of production data and online A/B testing.

Main Results:

  • The proposed framework significantly increases platform revenue.
  • Ad allocation is improved by matching user queries with more relevant ads.
  • Advertiser return on investment (ROI) is maintained.

Conclusions:

  • The developed framework effectively solves the large-scale sponsored search ad allocation problem.
  • The approach balances platform revenue maximization with advertiser and user considerations.
  • The model demonstrates practical viability through real-world testing.