Optimizing for Participation in Recommender System

In this paper, we document the development of a recommender system that provides inspiration to existing content uploaders and new future content uploaders to encourage participation. Our contributions are two-fold: 1) Inspiration Framework: We present a novel framework for building a recommender system that goes beyond traditional consumption-focused metrics, specifically addressing the need for creative inspiration to lower barriers for participation. This framework is adaptable in the design of large-scale recommender systems in other domains. 2) Empirical Evaluation: We conduct systematic evaluation via live experiments to prove the values of the proposed system in increasing daily participation and participants.