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Recommendation Signals

Updated February 22, 2026

This content is for informational purposes only and is not a substitute for professional advice.

Recommendation signals are the profile inputs and quality checks that move stack and supplement ranking.

Profile signals that shift ranking

The largest ranking signals are goal, stimulant sensitivity, contraindication state, current supplement list, consistency obstacles, and cycle preference.

These inputs are taken from onboarding and settings state.

Safety and exclusion signals

When contraindications are marked, high-risk classes can be removed from ranking.

When stimulant tolerance is low or set to avoid stimulants, stimulant-heavy options are reduced or excluded.

Evidence and data quality signals

Beyond profile fit, ranking includes evidence tier, quality score, safety-field completeness, and publication gate checks from the supplement dataset.

Higher-quality entries receive a scoring lift.

Why two users get different bundles

Even with the same goal, different contraindication flags, current supplements, or consistency obstacles can produce different top stacks.

Review AI Recommendation Generation for the full scoring pipeline.

Related

AI Recommendation Generation

Unfair generates recommendation bundles from your onboarding profile and local supplement catalog data.

AI Recommendations

AI recommendations in Unfair are structured stack and supplement rankings built from your profile and evidence metadata.

Ingredient Outcome Targets

Ingredient outcome targets link your selected goal to the supplement outcomes you are trying to improve.