Recommendation algorithms often optimize for engagement, not accuracy, boosting clicks while degrading long-term satisfaction.
Your most trusted 'friends' in feeds are not people but oddly correlated item similarities detected by hidden latent factors.
Cold-start plans rely on randomization and exploration, meaning novelty boosts your own future recommendations more than curated taste.
Small-scale experiments can flip outcomes: a tiny tweak in ranking weights can switch users from discovering to ignoring entire categories.
Recommendation algorithms often optimize for engagement, not accuracy, boosting clicks while degrading long-term satisfaction.
Your most trusted 'friends' in feeds are not people but oddly correlated item similarities detected by hidden latent factors.
Cold-start plans rely on randomization and exploration, meaning novelty boosts your own future recommendations more than curated taste.
Small-scale experiments can flip outcomes: a tiny tweak in ranking weights can switch users from discovering to ignoring entire categories.
