Content Promotion for Online Content Platforms with the Diffusion Effect

Yunduan Lin, Mengxin Wang, Heng Zhang, Renyu Zhang, Zuo-Jun Max Shen
{"title":"Content Promotion for Online Content Platforms with the Diffusion Effect","authors":"Yunduan Lin, Mengxin Wang, Heng Zhang, Renyu Zhang, Zuo-Jun Max Shen","doi":"10.1287/msom.2022.0172","DOIUrl":null,"url":null,"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 [Formula: see text]-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. Funding: R. Zhang is grateful for the financial support from the Hong Kong Research Grants Council General Research Fund [Grants 14502722 and 14504123] and the National Natural Science Foundation of China [Grant 72293560/72293565]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0172 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"3 9","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Manufacturing & Service Operations Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1287/msom.2022.0172","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

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 [Formula: see text]-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. Funding: R. Zhang is grateful for the financial support from the Hong Kong Research Grants Council General Research Fund [Grants 14502722 and 14504123] and the National Natural Science Foundation of China [Grant 72293560/72293565]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0172 .
利用扩散效应促进在线内容平台的内容推广
问题定义:内容推广政策对于在线内容平台提高内容消费和用户参与度至关重要。然而,传统的推广政策通常会忽视用户群的扩散效应。本文研究了在线内容平台的候选者生成和推广优化(CGPO)问题,强调了扩散效应的融入。方法/结果:我们提出了一个包含平台推广决策的扩散模型,以描述在线内容的采用过程。基于该扩散模型,我们将 CGPO 问题表述为一个具有非凸和非线性约束的混合整数程序,并证明该程序具有 NP 难度。此外,我们还研究了利用现有在线平台数据估算扩散模型参数的方法,并引入了新颖的双普通最小二乘法(D-OLS)估算器。我们证明了 CGPO 问题目标函数的亚模块性,这使我们能够找到一个高效的[公式:见正文]近似贪婪解。此外,我们还证明了 D-OLS 估计器与传统的普通最小二乘估计器相比,具有一致性和更小的渐近方差。通过利用一个大型视频共享平台的真实数据,我们证明了我们的扩散模型能有效描述在线内容的采用过程。与平台上实施的政策相比,我们提出的推广政策使总采用率提高了 49.90%。管理意义:我们的研究强调了扩散在网络内容中的重要作用,并为网络内容平台提供了可操作的见解,使其能够利用我们的扩散模型优化其内容推广政策。资助:R. Zhang 感谢香港研究资助局一般研究基金[Grants 14502722 and 14504123]和国家自然科学基金[Grant 72293560/72293565]的资助。补充材料:在线附录见 https://doi.org/10.1287/msom.2022.0172 。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信