Netflix and Cornell University researchers have exposed significant flaws in cosine similarity. Their study reveals that regularization in linear matrix factorization models introduces arbitrary scaling, leading to unreliable or meaningless cosine similarity results. These issues stem from the flexibility of embedding rescaling, affecting downstream tasks like recommendation systems.