Feature Diversity Is Not Monotone in Content Exploration: Evidence from Co-Adapting User–Creator Recommender Systems

Recommender systems influence both users, whose preferences can change with the content they see, and creators, who may adapt their content to the audiences they reach. We study how exploration affects recommendation diversity, the variety of content recommended to each user, while users remain influenced by their underlying interests and creators by their established specializations. We establish sufficient conditions under which intermediate exploration yields higher long-run recommendation diversity than both uniform recommendation, which splits each user's exposure equally across creators, and rates close to pure exploitation, which concentrates exposure on each user's highest-scoring creator. We also establish conditions for convergence to a unique equilibrium when exploration is sufficiently close to uniform. Simulations with synthetic vectors and representations learned from MovieLens show that intermediate exploration can improve recommendation diversity, with the benefit varying with the initialization of the dynamics and how strongly users and creators respond to recommendations.