lonelyrascal
lonelyrascal t1_j7vwy0c wrote
Reply to [P] Creating an embedding from a CNN by zanzagaes2
PCA has O(n^3) time complexity. Instead of doing that, why don't you pass the embedding through an autoencoder?
lonelyrascal t1_j7vwf3f wrote
Reply to comment by lonelyrascal in [D] Similarity b/w two vectors by TKMater
Well it also depends on whether you have sparse vectors or not... Some metrics work best for sparse vectors and other work for dense vectors. Check out scikit learn's pairwise distances metrics to understand better
lonelyrascal t1_j7vw6f4 wrote
Reply to [D] Similarity b/w two vectors by TKMater
Try cosine similarly?
lonelyrascal OP t1_j6odjl4 wrote
Reply to comment by curiousshortguy in [R] Question: what is the best approach to find similarity between a set of product titles and user query? by lonelyrascal
I have product brand, type and color other than titles. Yes I'll try cosine distances next. User queries are just tests done by me. Because there's no other way around except for A/B testing. Thank you.
lonelyrascal OP t1_j6od67i wrote
Reply to comment by marcingrzegzhik in [R] Question: what is the best approach to find similarity between a set of product titles and user query? by lonelyrascal
Thank you! I'll try word2vec
lonelyrascal t1_j7wp5yv wrote
Reply to comment by zanzagaes2 in [P] Creating an embedding from a CNN by zanzagaes2
Ok cool. Yeah keras has basic encoder decoder architecture in its documentation. If that's not something you like, you can always ask chatGPT ;)