How Does Competition Affect Exploration vs. Exploitation? A Tale of Two Recommendation Algorithms

• 成果类型: Article
• 署名作者: Cao, H. Henry; Ma, Liye; Ning, Z. Eddie; Sun, Baohong
• 署名单位: University System of Maryland; University of Maryland College Park; University of British Columbia
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.4722
• 发表日期: 2024
• 关键词: ai; BANDIT; Multihoming; recommendation algorithm; customization; PERSONALIZATION; content; COMPETITION; experimentation; Reinforcement Learning

摘要

Through repeated interactions, firms today refine their understanding of individual users' preferences adaptively for personalization. In this paper, we use a continuous time bandit model to analyze firms that recommend content to multihoming consumers, a representative setting for strategic learning of consumer preferences to maximize lifetime value. In both monopoly and duopoly settings, we compare a forward-looking recommendation algorithm that balances exploration and exploitation to a myopic algorithm that only maximizes the quality of the next recommendation. Our analysis shows that, compared with a monopoly, firms competing for users' attention focus more on exploitation than exploration. When users are impatient, competition decreases the return from developing a forward-looking algorithm. In contrast, development of a forward-looking algorithm may hurt users under monopoly but always benefits users under competition. Competing firms' decisions to invest in a forward-looking algorithm can create a prisoner's dilemma. Our results have implications for artificial intelligence adoption and for policy makers on the effect of market power on innovation and consumer welfare.