Netflix Study Shows Limits Of Cosine Similarity In Embedding Models

A new study by researchers at Netflix reveals that the popular metric of cosine similarity can yield arbitrary and meaningless results when measuring the proximity of low-dimensional embeddings of objects. This highlights the importance of understanding how deep learning models are trained and their unintended effects on similarity measures.