Feat/microservices/personalization engine 364

[mijinummi]

Description

Closes #364

This PR addresses issue #364 by establishing the foundational personalization-service architecture layer. This system tracks user clickstream trends, trains categorization affinities to balance user weights, manages dynamic feature layout flags, and distributes targeted yield tier profiles without impacting synchronous runtime logic.

Changes Introduced

• Dynamic Learning State Matrix: Leveraged jsonb indexing models inside relational databases to avoid locking constraints on flexible metadata fields.
• Consistent Testing Tracks: Programmed hashing routines to prevent cross-contamination across variant tracks.
• Automated Pricing Tiers: Built rule matching steps that transition users across pricing models depending on behavioral values.

Acceptance Criteria Verified

• Application configurations load and run securely in complete isolation from sibling processes.
• Profiling lookups adapt schema elements immediately following weight score modifications.
• Randomization configurations route tracking records to proper variant pools cleanly.

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