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中国管理科学 ›› 2025, Vol. 33 ›› Issue (11): 162-172.doi: 10.16381/j.cnki.issn1003-207x.2023.1159

• • 上一篇    

移动健康平台推荐系统部署及成本策略研究

李佩伦, 尹秋菊, 颜志军()   

  1. 北京理工大学管理学院,北京 100081
  • 收稿日期:2023-07-11 修回日期:2024-01-25 出版日期:2025-11-25 发布日期:2025-11-28
  • 通讯作者: 颜志军 E-mail:yanzhijun@bit.edu.cn
  • 基金资助:
    国家自然科学基金项目(72072011);国家自然科学基金项目(72110107003);国家自然科学基金项目(72431003);中央高校基本科研业务费专项(2023CX13026)

Research on Recommender System Deployment and Cost Strategy in Mobile Health Platform

Peilun Li, Qiuju Yin, Zhijun Yan()   

  1. School of Management,Beijing Institute of Technology,Beijing 100081,China
  • Received:2023-07-11 Revised:2024-01-25 Online:2025-11-25 Published:2025-11-28
  • Contact: Zhijun Yan E-mail:yanzhijun@bit.edu.cn

摘要:

移动健康平台为用户提供了大量运动课程,并常常通过部署推荐系统提高用户对运动课程的认知水平,帮助用户更好地进行个人健康管理。推荐系统的部署改变了市场竞争环境,课程提供商需要据此制定合理的价格与成本策略以占据竞争市场的优势地位。本文建立了有无部署推荐系统和差异性成本策略(高或低)的价格博弈模型,分析推荐系统对平台和课程提供商的利润影响,探索平台部署推荐系统的前提条件以及课程提供商应采用的成本策略。结果发现:不论课程提供商采用何种成本策略,推荐系统均可提升其利润和平台利润;较高的用户健康价值转换系数和高市场认知水平,将促使课程提供商采用高成本策略;而当用户从课程中获得的固定健康价值较高时,低成本策略是课程提供商的最优选择。

关键词: 移动健康平台, 课程推荐, 价格竞争, 成本策略

Abstract:

The mobile health platforms offer users lots of exercise courses. And the platforms often employ recommender systems to enhance users' awareness of fitness exercise courses, helping them better manage their personal health. Recommender systems can predict the users’ preferences based on their historical data and recommend suitable fitness courses. One of the key objectives of deploying recommender systems is to earn higher commission fees from course providers. However, if the systems intensify market competition and subsequently reduces course sales, platform’s commission fees will correspondingly decline. This potential decrease in platform fees may prevents deploying recommender systems.The impact of recommender systems on the competitive market environment remains ambiguous. While enhancing product visibility, they also intensify competition among substitutable products. In the mobile health platforms, recommender systems increase user awareness of course offerings and expand the pool of potential customers, but they also heighten the competition among course providers. Course providers must adjust their business strategies according to the market shifting caused by recommender systems. Apart from attracting users, they need set reasonable prices to expand potential market from the extra exposure provided by the recommender system. Cost strategy of providers is intricately linked to the product price strategy. Effective cost control enhances the market competitiveness of products. A low-cost approach to producing fitness courses makes it more feasible to lower sales prices. Such low-price courses are more likely to be recommended but not necessarily favored by users. Conversely, high-quality fitness courses resulting from high-cost strategies offer greater value to users. High-cost courses provide users more health benefits, but their corresponding high selling prices reduce the likelihood of obtaining additional exposure from recommender systems. Three questions are primarily investigated: Under what conditions does a platform have the incentive to deploy a recommender system? How does the recommender system affect market price competition and course providers' sales profits under different cost strategies? And under various levels of user awareness and market conditions, how should course providers decide their cost strategies?The results show that (1) Regardless of whether course providers adopt a high- or low-cost strategy, deploying a recommender system can enhance platform profits. (2) When providers uniformly adopt a high-cost strategy, the recommender system intensifies price competition among them, but simultaneously increases user demand and provider profits. (3) When providers uniformly opt for a low-cost strategy, the recommender system mitigates price competition among them. The systems may not necessarily increase user demand but can still enhance provider profits. (4) The choice of cost strategy for providers is jointly influenced by user characteristics and market conditions. When the degree of course mismatch, production costs, user health value conversion coefficients, or market cognitive levels are high, providers should choose a high-cost strategy. Conversely, when users gain a high fixed health value from courses, adopting a low-cost strategy is beneficial to provider profits.

Key words: mHealth platforms, course recommender system, price competition, cost strategy

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