Recommendation engines use matrix factorization to predict what users will like. Standard approach: factorize one huge matrix. CRT approach: decompose into 6 tiny independent matrices, factor each in its own channel, reconstruct via CRT. Block-diagonal: zero cross-channel gradient leakage. L=11 = free trust verification.
Random seed:
Generates a 6x8 sparse ratings matrix. CRT decomposes, factors 6 channels independently, predicts missing entries. Gold = predicted. Green = known.
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— Anton Alexandrovich Lebed
Source code · Public domain (CC0)
Contributions in equal measure: Anthropic's Claude, Anton A. Lebed, and the giants whose shoulders we stand on.
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