Content discovery is treated as an independent objective because relevance optimization alone tends to surface familiar or already-popular content, which does not necessarily help users find new items they would enjoy. Accelerating content discovery specifically targets catalog breadth, helping users encounter content beyond their established preferences or the platform's most-trafficked titles. This distinction matters because a system optimizing purely for predicted relevance can inadvertently narrow the user's exposure over time. Platforms should track content discovery metrics separately from relevance scores to ensure their recommendation system is genuinely expanding user awareness of available catalog rather than reinforcing existing patterns.