Hitting the Books: How to build a music recommendation 'information-space-beast' | Engadget

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Hitting the Books: How to build a music recommendation 'information-space-beast' | Engadget
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Hitting the Books: How to build a music recommendation 'information-space-beast'

by Nick Seaver, published by The University of Chicago Press. © 2022 by The University of Chicago. All rights reserved.“We are now at the dawn of the age of infinitely connected music,” the data alchemist announced from beneath the Space Needle. Glenn McDonald had chosen his title himself, preferring “alchemy,” with its esoteric associations, over the now-ordinary “data science.

Elsewhere, McDonald would describe the world of music as though it were a landscape: “Follow any path, no matter how unlikely and untrodden it appears, and you’ll find a hidden valley with a hundred bands who’ve lived there for years, reconstructing the music world in methodically- and idiosyncratically-altered miniature, as in Australian hip hop, Hungarian pop, microhouse or Viking metal.”

Although his language may have been unusually poetic, McDonald was expressing an understanding of musical variety that is widely shared among the makers of music recommendation: Music exists in a kind of space. That space is, in one sense, fairly ordinary — like a landscape that you might walk through, encountering new things as you go.

But despite their familiarity, spaces like these are strange: similarities can be found anywhere, and points that seemed far apart might suddenly become adjacent. If you ask, you will learn that all of these spatial representations are mere reductions of something much more complex, of a space comprising not two or three dimensions but potentially thousands of them.

For many critics, this “geometric rationality” of machine learning makes it anathema to “culture” per se: it quantifies qualities, rationalizes passions, and plucks cultural objects from their everyday social contexts to relocate them in the sterile isolation of a computational grid.

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