Content Promotion for Online Content Platforms with the Diffusion Effect

• 成果类型: Article
• 署名作者: Lin, Yunduan; Wang, Mengxin; Zhang, Heng; Zhang, Renyu; Shen, Zuo-Jun Max
• 署名单位: University of California System; University of California Berkeley; University of Texas System; University of Texas Dallas; Arizona State University; Arizona State University-Tempe; Chinese University of Hong Kong; University of Hong Kong; University of Hong Kong
• 刊物名称: M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1523-4614
• DOI: 10.1287/msom.2022.0172
• 发表日期: 2024
• 关键词: online content; diffusion modeling; promotion optimization; Approximation algorithms

摘要

Problem definition: Content promotion policies are crucial for online content forms to improve content consumption and user engagement. However, traditional promotion policies generally neglect the diffusion effect within a crowd of users. In this paper, 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 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 fusion 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 have smaller asymptotic variances than traditional ordinary least squares estimators. By lizing 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 49.90%. Managerial implications: Our research highlights the essential role of diffusion online content and provides actionable insights for online content platforms to optimize content promotion policies by leveraging our diffusion model.