Is cosine similarity always the best choice?
Choosing the right similarity metric for your recommendation system
Developing an effective recommendation system starts with creating robust vector embeddings. While many default to cosine similarity for comparing vectors, choosing the right metric is crucial and should be tailored to your specific use case. For instance, cosine similarity focuses on pattern recognition by emphasizing the direction of vectors, whereas Euclidean distance also factors in magnitude.
๐๐ฆ๐บ ๐๐ช๐ฎ๐ช๐ญ๐ข๐ณ๐ช๐ต๐บ ๐๐ฆ๐ต๐ณ๐ช๐ค๐ด ๐ง๐ฐ๐ณ ๐๐ฆ๐ค๐ฐ๐ฎ๐ฎ๐ฆ๐ฏ๐ฅ๐ข๐ต๐ช๐ฐ๐ฏ ๐๐บ๐ด๐ต๐ฆ๐ฎ๐ด:
๐๐ผ๐๐ถ๐ป๐ฒ ๐ฆ๐ถ๐บ๐ถ๐น๐ฎ๐ฟ๐ถ๐๐: Focuses on directional relationships rather than magnitude
โข Content-based recommendations prioritizing thematic alignment
โข Vision Transformer (CLIP, ViT, BEiT) embeddings where directional relationships matter more than magnitude
๐๐๐ฐ๐น๐ถ๐ฑ๐ฒ๐ฎ๐ป ๐๐ถ๐๐๐ฎ๐ป๐ฐ๐ฒ: Accounts for both direction and magnitude
โข Product recommendations measuring preference intensity
โข CNN feature comparisons (ResNet, VGG) where spatial relationships and magnitude differences represent visual similarity
An animation helps to understand it in a better way. You can use the code for animation to try out more things.
You can explore more, such as ๐ ๐ถ๐ป๐ธ๐ผ๐๐๐ธ๐ถ ๐๐ถ๐๐๐ฎ๐ป๐ฐ๐ฒ and ๐๐ฎ๐บ๐บ๐ถ๐ป๐ด ๐๐ถ๐๐๐ฎ๐ป๐ฐ๐ฒ. I recommend conducting comparative evaluations through ๐/๐ ๐๐ฒ๐๐๐ถ๐ป๐ด to determine which metric delivers the most relevant recommendations for your specific visual recommendation application.