Ephemeral State-Dependent Recommendation for Digital Content
提出一个根据消费者短暂状态（专注或探索）自适应推荐同化或多样化内容的框架，通过随机现场实验发现状态匹配方案能提升参与度和收入，但部分偏好流动的消费者反而从不匹配方案中获益。
Practice- and Policy-oriented Abstract Building upon recent advances in consumption theories, we propose an ephemeral state-dependent framework for digital content recommendations. The framework accentuates a critical, yet understudied, interplay between a firm’s recommendation strategy (assimilation or diversification) and a consumer’s ephemeral state (fixation or foraging). The framework adaptively recommends either assimilated or diversified content based on a consumer’s ephemeral state. Through a randomized field experiment, we provide compelling evidence that state-dependent schemes can enhance engagement and revenue. Although the congruent scheme (i.e., assimilation when fixation, diversification when foraging) generally outperforms the incongruent one, contributing a 7.3% ($19.73 million) annual revenue lift for the platform, our findings underscore the necessity for nuanced personalization. Specifically, consumers with broader, more fluid preferences benefit more from the incongruent scheme (i.e., assimilation when foraging, diversification when fixation), challenging the prevailing assumption that congruence is always optimal. Our research not only adds to the theoretical understanding of consumer behavior in digital content consumption, but also offers actionable insights for designing more effective recommender systems. By accounting for consumer heterogeneity and considering the broader implications of our proposed recommendation framework, including spillover effects, our findings have the potential to influence industry practices and future academic inquiry in this rapidly evolving field.