Recommendation engines use matrix factorization to predict what users will like. Standard approach: factorize one huge matrix. CRT approach: decompose into 7 tiny independent matrices (mod 8, 9, 25, 49, 11, 13, 17), factor each in its own channel, reconstruct via CRT over Z/214,414,200. Block-diagonal: zero cross-channel gradient leakage. Three parity channels (mod 11, 13, 17) give free trust verification.
Random seed:
Generates a 6x8 sparse ratings matrix. CRT decomposes, factors 7 channels independently, predicts missing entries. Gold = predicted. Green = known.
Source code · Public domain (CC0)
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