Content Promotion for Online Content Platforms with the Diffusion Effect

Manufacturing & Service Operations Management

48 Pages Posted: 16 Jun 2021 Last revised: 20 Feb 2024

See all articles by Yunduan Lin

Yunduan Lin

University of California, Berkeley - Department of Civil and Environmental Engineering

Mengxin Wang

University of Texas at Dallas - Naveen Jindal School of Management

Heng Zhang

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

Renyu (Philip) Zhang

The Chinese University of Hong Kong

Zuo-Jun Max Shen

University of California, Berkeley - Department of Industrial Engineering & Operations Research (IEOR)

Date Written: June 9, 2021

Abstract

Problem Definition: Content promotion policies are crucial for online content platforms to improve content consumption and user engagement. However, traditional promotion policies generally neglect the diffusion effect within a crowd of users. In this paper, we study the candidate generation and promotion optimization (CGPO) problem for an online content platform, emphasizing the incorporation of the diffusion effect.
Methodology: We propose a diffusion model that incorporates platform promotion decisions to characterize the adoption process of online content. Based on this diffusion model, we formulate the CGPO problem as a mixed-integer program with nonconvex and nonlinear constraints, which is proved to be NP-hard. Additionally, we investigate methods for estimating the diffusion model parameters using available online platform data and introduce novel double ordinary least squares (D-OLS) estimators.
Results: We prove the submodularity of the objective function for the CGPO problem, which enables us to find an efficient (1-1/e)-approximation greedy solution. Furthermore, we demonstrate that the D-OLS estimators are consistent and have smaller asymptotic variances than traditional OLS estimators. By utilizing real data from a large-scale video-sharing platform, we show that our diffusion model effectively characterizes the adoption process of online content. Compared to the policy actually implemented on the platform, our proposed promotion policy increases total adoption by 49.90%.
Managerial Implications: Our research highlights the essential role of diffusion in online content and provides actionable insights for online content platforms to optimize their content promotion policies by leveraging our diffusion model.

Keywords: Online Content, Diffusion Modelling, Promotion Optimization, Approximation Algorithms

Suggested Citation

Lin, Yunduan and Wang, Mengxin and Zhang, Heng and Zhang, Renyu and Shen, Zuo-Jun Max, Content Promotion for Online Content Platforms with the Diffusion Effect (June 9, 2021). Manufacturing & Service Operations Management, Available at SSRN: https://ssrn.com/abstract=3863104 or http://dx.doi.org/10.2139/ssrn.3863104

Yunduan Lin (Contact Author)

University of California, Berkeley - Department of Civil and Environmental Engineering ( email )

Berkeley, CA
United States

Mengxin Wang

University of Texas at Dallas - Naveen Jindal School of Management ( email )

P.O. Box 830688
Richardson, TX 75083-0688
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

Zuo-Jun Max Shen

University of California, Berkeley - Department of Industrial Engineering & Operations Research (IEOR) ( email )

IEOR Department
4135 Etcheverry Hall
Berkeley, CA 94720
United States

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
354
Abstract Views
1,366
Rank
152,055
PlumX Metrics