Content Promotion for Online Content Platforms with the Diffusion Effect

Yunduan Lin (), Mengxin Wang (), Heng Zhang (), Renyu Zhang () and Zuo-Jun Max Shen ()
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Yunduan Lin: Civil and Environmental Engineering Department, University of California, Berkeley, Berkeley, California 94720
Mengxin Wang: Naveen Jindal School of Management, The University of Texas at Dallas, Richardson, Texas 75080
Heng Zhang: W. P. Carey School of Business, Arizona State University, Tempe, Arizona 85287
Renyu Zhang: Chinese University of Hong Kong Business School, The Chinese University of Hong Kong, Hong Kong, China
Zuo-Jun Max Shen: Faculty of Engineering and Faculty of Business and Economics, University of Hong Kong, Hong Kong, China

Manufacturing & Service Operations Management, 2024, vol. 26, issue 3, 1062-1081

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/results : 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. 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 ordinary least squares 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 with the policy implemented on the platform, our proposed promotion policy increases total adoptions 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 modeling; promotion optimization; approximation algorithms (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:26:y:2024:i:3:p:1062-1081

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