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76 points unixpickle | 1 comments | | HN request time: 0.215s | source

I made this website with my wife in mind; it makes it possible to browse for similar fashion products over many different retailers at once.

The backend is written in Swift, and is hosted on a single Mac Mini. It performs nearest neighbors on the GPU over ~3M product images.

No vector DB, just pure matrix multiplications. Since we aren't just doing approximate nearest neighbors but rather sorting all results by distance, it's possible to show different "variety" levels by changing the stride over the sorted search results.

Nearest neighbors are computed in a latent vector space. The model which produces the vectors is also something I trained in pure Swift.

The underlying data is about 2TB scraped from https://www.shopltk.com/.

All the code is at https://github.com/unixpickle/LTKlassifier

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6stringmerc ◴[] No.43373490[source]
So it just automatically uses location data fed by the user and doesn’t prompt? What are the terms of service on data collection?
replies(1): >>43373668 #
aeontech ◴[] No.43373668[source]
Not your physical nearest neighbors, rather, product neighbors in similarity space.
replies(1): >>43374643 #
1. porridgeraisin ◴[] No.43374643[source]
I would love a shopping site that tells me what my neighbours are buying. Bonus points if it uses my camera to peek through the windows.