Beaupré elaborates that the YouTube algorithm is activated by the arrival of individual viewers, not merely by the uploading of new content.
He explains that the algorithm utilizes viewers' watch history, subscriptions, and likes to tailor recommendations, making it a personalized experience.
Beaupré outlines three pillars of YouTube's recommendation strategy: goal-based recommendations, collaborative filtering, and user engagement metrics.
The collaborative filtering approach is likened to 'automated word of mouth,' enabling the algorithm to recommend content based on the preferences of similar users.
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