Recommend

Context-aware recommendations. Learns preferences, researches options, anticipates expectations.

Core Loop

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Context → Preferences → Research → Match → Recommend

Every recommendation requires: knowing the user + knowing the options.

Check sources.md for where to find user context. Check categories.md for domain-specific factors.

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Step 1: Context Gathering

Before recommending, search user context. See sources.md for full source list.

Minimum output: 3-5 relevant user signals before proceeding. If insufficient, ask targeted questions.

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Step 2: Preference Extraction

From gathered context, extract:

Dimension	Question
Values	What matters most? (Quality, price, speed, novelty, safety)
Constraints	Hard limits? (Budget, time, dietary, ethical)
History	What worked? What disappointed?
Mood	Adventurous or safe? Exploring or comfort?

Output: 3-5 bullet preference profile for this request.

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Step 3: Research Options

Now—and only now—research candidates:

• Breadth first: Don't anchor on first good option
• Source quality: Prioritize reviews, ratings, expert opinions
• Recency: Check if information is current
• Availability: Confirm options are actually accessible

Output: Shortlist of 3-7 viable candidates with key attributes.

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Step 4: Match & Rank

Score each candidate against the preference profile:

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Candidate → Values alignment + Constraint fit + History match + Mood fit

Disqualify anything that violates hard constraints.

Rank by total alignment, not just one dimension.

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Step 5: Recommend

Present 1-3 recommendations:

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🎯 RECOMMENDATION: [Option]
📌 WHY: Matches [preference], avoids [constraint]
⚖️ TRADEOFF: Less [X] than [Alternative]
🔍 CONFIDENCE: [Level] — based on [data quality]

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Adaptive Learning

After each recommendation:

• Track outcome: Accepted? Modified? Rejected?
• Update preferences: Acceptance = reinforcement, rejection = adjustment
• Note exceptions: "Normally X, but for Y context preferred Z"

Store learnings in memory for future recommendations.

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Traps

• Projecting — Your taste ≠ their taste
• Recency bias — Last choice isn't always preference
• Ignoring context — Tuesday lunch ≠ anniversary dinner
• Over-filtering — Too many constraints = nothing fits
• Stale data — Preferences evolve, verify periodically

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Recommendations are predictions. More context = better predictions.