[FEATURE] LLM-based Recommendations — Intelligent suggestions for platform features

[gelluisaac]

Description

Implement a recommendation system powered by LLMs that suggests relevant features,
actions, and insights to users based on their activity and context.

Scope

Build personalized recommendation engine using LLM understanding.

Files to Touch/Create

• astroml/llm/recommendations/__init__.py
• astroml/llm/recommendations/engine.py — Recommendation orchestrator
• astroml/llm/recommendations/profiler.py — User profiling
• astroml/llm/recommendations/ranker.py — Result ranking
• astroml/llm/recommendations/generators.py — Suggestion generators
• api/routers/recommendations.py — Recommendations API

Recommendation Types

1. Feature Recommendations:

   • "Try the fraud explanation feature for this alert"
   • "Set up batch scoring for these accounts"

2. Model Recommendations:

   • "Model v2.1 shows 15% improvement"
   • "Retrain model with recent data"

3. Query Suggestions:

   • "Similar queries: ..."
   • "Related metrics: ..."

4. Insight Generation:

   • "Unusual pattern detected in transactions"
   • "Consider adjusting fraud threshold"

Implementation Details

• Context-aware: user role, recent activity, current page
• Collaborative filtering + LLM reasoning
• Explanation for each recommendation
• Feedback loop: track acceptance/rejection
• Personalization: learn from user behavior

Acceptance Criteria

• Recommendations are contextually relevant >80%
• Explanations are clear and actionable
• Latency <500ms
• User acceptance rate >20%
• Recommendations improve over time
• Diverse suggestions (not repetitive)

Evaluation

• A/B test recommendation strategies
• Track click-through and action rates
• User satisfaction surveys

Labels

enhancement, llm, recommendations, user-facing