Targeted Boosts
Rule-based adjustments that slightly increase (or decrease) scores for specific dimensions to reflect user priorities, context, or safety considerations.
What This Really Means
Boosts can support personalization—for example, emphasizing compatibility on communication style for people who value directness.
They should be bounded and auditable to avoid hidden manipulation.
In international products, boosts may also be used to support local cultural calibration while preserving core consent and safety standards.
Examples
Boosting matches with similar initiation style for users who report high importance of communication
Reducing scores when a key boundary preference conflicts
Increasing weight on relationship form preference in regions where exclusivity norms differ.
Common Misunderstandings
Tap each myth to reveal the reality
Targeted Boosts is about rule-based adjustments that slightly increase (or decrease) scores for specific dimensions to reflect user priorities, context, or safety, and it doesn’t imply that boosts are the same as cheating the model.
Targeted Boosts should never override consent or comfort, and safety stays the priority.
Targeted Boosts doesn’t guarantee outcomes like that, and it mainly describes rule-based adjustments that slightly increase (or decrease) scores for specific dimensions to reflect user priorities, context, or safety.
Targeted Boosts describes rule-based adjustments that slightly increase (or decrease) scores for specific dimensions to reflect user priorities, context, or safety, so it doesn’t mean that boosts remove the need for validity testing.
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Inside LoveIQ
We identify patterns related to Targeted Boosts by analyzing responses in our assessment modules, helping you understand your unique relationship dynamics.
Sample visualization of a gap metric.
“You don't need to label yourself. These terms help describe patterns — not define you.”
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