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Personalized recommendation systems for credentials analyze user behavior (e.g., past verifications, preferred services) and contextual data (e.g., location, time) to suggest relevant VCs. For example, a frequent traveler might receive airport lounge access credentials proactively. Machine learning models refine recommendations over time, while decentralized identifiers (DIDs) ensure privacy by keeping data user-controlled. Users can customize suggestions or opt out, balancing personalization with autonomy.
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