Corus logged 250,000 downloads in its debut month, confirming that listeners prefer algorithm-free discovery. The app implements seven techniques to inject controlled randomness into a curated listening flow. These include on-device sampling, user-driven seed tracks, and adaptive playlist generation. By avoiding a global recommendation engine, Corus reduces algorithmic bias and gives developers a simpler model to integrate into existing services. If you're building a music recommendation layer, consider how a lightweight, randomness-based approach can improve discoverability without the overhead of a full-blown recommendation system.
Annabel Baker
Posted on Originally published at swiftpath.site
7 Surprising Ways Corus Rewrites Music Discovery
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