feat(personalization): add AI-powered dashboard personalization for #577

[Theonlyayodeji]

Overview

This PR adds an AI-style Dashboard Personalization system that learns from user behavior and automatically customizes the dashboard layout, widgets, and data displays to match individual preferences and workflows.

Related Issue

Closes #577

Changes

Personalization Engine

• [ADD] \src/lib/personalizationEngine.ts\

  • Preference learning using interaction data (tab visits, widget usage, feature usage) and explicit feedback
  • Layout optimization algorithm based on usage patterns with compactness scoring
  • Widget recommendation system with scoring by relevance, frequency, corrections, and explicit preferences
  • Gradual personalization with user control (learning on/off, transparency levels)
  • Local-first storage via IndexedDB — no data sent to external servers
  • User detection (power user vs. casual user) and peak usage hour identification

• [ADD] \src/components/dashboard/PersonalizationPanel.tsx\

  • New AI Personalization dashboard tab with three sub-tabs: Insights, Recommendations, and Settings
  • KPI strip showing efficiency gain, interactions, tabs visited, widgets used, and learning status
  • Usage profile section with badges (Power User, Getting Started, Layout Preference, Peak Hours)
  • Most visited tabs and most used widgets breakdown
  • Widget recommendations ranked by relevance score with Accept/Dismiss controls
  • Layout optimization suggestions based on user behavior analysis
  • Full settings controls: learning toggle, transparency level selector, and reset with confirmation

• [MODIFY] Routing / nav

  • \DashboardLayout.tsx, \Sidebar.tsx, \MobileSidebar.tsx, \usePreload.ts\ -- register \personalization\ tab

• [ADD] \src/lib/tests/personalizationEngine.test.ts\

  • 29 tests covering profile creation, interaction recording, widget recommendations, personalization stats, efficiency scoring, peak hour detection, power/casual user detection, and layout compactness scoring

Verification Results

\
npm test -- src/lib/tests/personalizationEngine.test.ts
29/29 passed

Lint: 0 errors, 0 warnings
Type-check: No new errors
\\

Acceptance Criteria	Status
Personalization improves user efficiency by 30%	Estimated efficiency gain calculated from interaction patterns, widget adoption rate, and correction learning
Users can override suggestions	Accept/Dismiss controls on all recommendations; learning can be paused entirely
System learns from corrections	Corrections penalize widget scores; accepted suggestions are boosted
Personalization is transparent	Full/summary/minimal transparency levels; How Personalization Works explainer section; privacy notice

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