Ephemeral State-Dependent Recommendation for Digital Content.

The article focuses on developing and empirically testing an ephemeral state-dependent framework for digital content recommender systems that adaptively pairs a firm's recommendation strategy—assimilation (consistency) or diversification (variety)—with a consumer's transient consumption state, defined as fixation (focused on one genre) or foraging (exploring multiple genres). A large-scale randomized field experiment conducted with a leading e-book platform demonstrates that state-dependent recommendation schemes outperform state-independent ones, with the congruent scheme (assimilation during fixation, diversification during foraging) generally yielding a 7.3% increase in annual revenue. However, consumer heterogeneity is significant: consumers with broader or more fluid preferences respond better to the incongruent scheme, indicating the need for nuanced personalization. The study also reveals positive spillover effects on nonrecommended content consumption, suggesting broader benefits of adaptive, behavior-informed recommendation strategies for digital content platforms.