Cold Start to Improve Market Thickness on Online Advertising Platforms: Data-Driven Algorithms and Field Experiments

71 Pages Posted: 19 Nov 2020 Last revised: 10 Dec 2021

See all articles by Zikun Ye

Zikun Ye

University of Illinois at Urbana-Champaign

Dennis Zhang

Washington University in St. Louis - John M. Olin Business School

Heng Zhang

Supply Chain Management Department - W.P.Carey School of Business

Renyu (Philip) Zhang

The Chinese University of Hong Kong; New York University Shanghai

Xin Chen

University of Illinois at Urbana-Champaign

Zhiwei Xu

affiliation not provided to SSRN

Date Written: October 1, 2020

Abstract

Cold start describes a commonly recognized challenge for online advertising platforms: with limited data, the machine learning system cannot accurately estimate the click-through rates (CTR) of new ads and, in turn, cannot efficiently price these new ads or match them with platform users. Traditional cold start algorithms often focus on improving the learning rates of CTR for new ads to improve short-term revenue, but unsuccessful cold start can prompt advertisers to leave the platform, decreasing the thickness of the ad marketplace. To address these issues, we build a data-driven optimization model that captures the essential trade-off between short-term revenue and long-term market thickness on the platform. Based on duality theory and bandit algorithms, we develop the Shadow Bidding with Learning (SBL) algorithms with a provable regret upper bound of O(T^{2/3}K^{1/3}(logT)^{1/3}d^{1/2}), where $K$ is the number of ads and d captures the error magnitude of the underlying machine learning oracle for predicting CTR. Our proposed algorithms can be implemented in a real online advertising system with minimal adjustments. To demonstrate this practicality, we have collaborated with a large-scale video-sharing platform, conducting a novel, two-sided randomized field experiment to examine the effectiveness of our SBL algorithm. Our results show that the algorithm increased the cold start success rate by 61.62% while compromising short-term revenue by only 0.717%. Our algorithm has also boosted the platform’s overall market thickness by 3.13% and its long-term advertising revenue by (at least) 5.35%. Our study bridges the gap between the bandit algorithm theory and the cold start practice, highlighting the value of well-designed cold start algorithms for online advertising platforms.

Keywords: Cold Start Problem, Online Advertising, Contextual Bandit, Two-Sided Field Experiment

Suggested Citation

Ye, Zikun and Zhang, Dennis and Zhang, Heng and Zhang, Renyu and Chen, Xin and Xu, Zhiwei, Cold Start to Improve Market Thickness on Online Advertising Platforms: Data-Driven Algorithms and Field Experiments (October 1, 2020). Available at SSRN: https://ssrn.com/abstract=3702786 or http://dx.doi.org/10.2139/ssrn.3702786

Zikun Ye (Contact Author)

University of Illinois at Urbana-Champaign ( email )

601 E John St
Champaign, IL 61820
United States

Dennis Zhang

Washington University in St. Louis - John M. Olin Business School ( email )

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

Heng Zhang

Supply Chain Management Department - W.P.Carey School of Business ( email )

Tempe, AZ
United States

Renyu Zhang

The Chinese University of Hong Kong ( email )

Shatin, N.T.
Hong Kong, Hong Kong
China

HOME PAGE: http://rphilipzhang.github.io/rphilipzhang/index.html

New York University Shanghai ( email )

1555 Century Avenue
Shanghai, 200122
China
86-21-20595135 (Phone)

HOME PAGE: http://rphilipzhang.github.io/rphilipzhang/index.html

Xin Chen

University of Illinois at Urbana-Champaign ( email )

601 E John St
Champaign, IL 61820
United States

Zhiwei Xu

affiliation not provided to SSRN

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