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Reference Implementation — Graph Recommendations

Candidate generation with graph neighborhoods, then rank with hybrid business and ML signals.

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Overview

Reference Implementation — Graph Recommendations

Candidate generation with graph neighborhoods, then rank with hybrid business and ML signals.

Why it matters

Recommendation quality improves when graph candidate generation captures relational context before expensive ranking.

Going deeper

Practical pipeline:

  1. Generate candidates by neighborhood/path motifs.
  2. Filter by policy, inventory, and eligibility.
  3. Rank with hybrid model (graph + behavior + business constraints).
  4. Log explanations as path snippets for trust and debugging.

Analogy

Graph recommendations are a matchmaker who shortlists, then lets a panel decide. The graph is brilliant at the shortlist — 'people in your neighbourhood who bought what you're looking at' — but final ranking, pricing and eligibility belong to the panel (the ML + business rules). Ask the matchmaker to set prices and you'll regret it.

Pitfalls — what breaks when this is weak

  • Pushing graph logic into final ranking/pricing. Wrong layer. Fix: stop graph at candidate generation + relationship features.
  • No eligibility/inventory filter. Recommends the unavailable. Fix: filter candidates before ranking.
  • No explanations. Users distrust opaque recs. Fix: log path snippets as the 'why'.

Make it stick

Use the prompts below to anchor reference implementation — graph recommendations to a real graph you own.

  • ›Where does graph candidate generation end and ML/business ranking begin in your stack?
  • ›Which eligibility and inventory filters must run before ranking?
  • ›Could you show a path-based 'why' for each recommendation?

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