I liked this post a lot. I liked that they are doing this with the simplest and most well-understood techniques, matrix factorization+PCA. Though I'm sure the

I liked this post a lot. I liked that they are doing this with the simplest and most well-understood techniques, matrix factorization+PCA. Though I'm sure they're also trying end-to-end extra-deep programmatic multi-media networks with new kinds of convolutional layers, fancy residual connections, an experimental batchnorm variant, etc... I'd love to see if they squeeze more juice out of that. As we make more of these semantic meaning-spaces like word2vec, or the meaning space of "photos of airbnb rooms", I think we could use some foundational UI design work to navigate them better. Like, now you can navigate everything on stitchfix through a small 50-d space where every component is nearly orthogonal and can kind of be interpreted ahead of time+labelled. Is "a binary tree in the form of playing N questions" the best way to go through them, or are there other options? Should we allow navigation in more than one dimensions at a time? Could we start with a few landmark, prototypical elements in each dimension? Have the user progressively clamp down the range in components, and show a PCA of the remaining components each time? Would a 3rd dimension (with VR?) help separate more dimensions at a time, giving you 50% more dimensions to show items on? Computers are increasingly understanding our world and so far they understand it through these meaning spaces, so I think this would be incredibly important for the future of UI design and computer interpretability.