Trading off Quality and Variety in Recommendation Systems on Content Platforms

User-generated content platforms use advanced recommendation algorithms to deliver content from a vast number of creators to a wide audience with diverse preferences. Recommendation systems match their offers to users’ tastes while taking into account content quality. The literature has focused on the impact of recommendation systems on demand-side factors, such as the quality and variety of users’ content consumption. However, the literature has largely overlooked their influence on supply-side factors, including creators’ entry and quality decisions, and how they interact with consumers’ content diet. The literature has also neglected the hit-driven nature of content production, whereby very few content generates disproportionate consumer interest. We address the above omissions and explicitly model creators’ entry. We reveal a tradeoff between the provision of content quality and variety on a platform. While increasing the recommended content’s fit to users’ preferences benefits consumers and the platform, pushing higher quality content may hurt consumer welfare by reducing the variety of content produced and the emergence of trendy content on the platform, potentially lowering consumers’ total valuation of the platform. We show that whether the recommendation system should emphasize the quality dimension depends on consumers’ valuations for content match and trendiness, the heterogeneity level of their valuations, and creators’ barrier to enter the platform. Additionally, consumption variety is a U-shaped function of the emphasis the recommendation system puts on content quality.