01Personalization is three problems, not one
- Discovery — what should we show this person on the home page?
- Ranking — given a category, which products go first?
- Search relevance — given a query, what is most relevant for this person?
AI Business · July 15, 2025
AI for Retail: Personalization Engine Tutorial
A practical guide to building a retail personalization engine — recommendations, ranking, and the cold-start tricks that make it work for new visitors.
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Binate AI
July 15, 2025
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02A pragmatic recommendation stack
- 1
Candidate generation
Retrieve 200–500 plausible products fast. Methods: co-purchase, embeddings, popularity in segment.
- 2
Ranking
Score candidates with a learning-to-rank model trained on clicks, add-to-cart, and purchases.
- 3
Diversification
Avoid showing five variants of the same item. Maximum marginal relevance or category capping.
- 4
Business rules layer
Stock cutoffs, margin floors, promotion enforcement. Always last.
03Solving the cold-start problem
New visitors have no history. New products have no interactions. Both must work on day one.
New user
Tactics that work
- Geolocation + device + referrer signals
- Best sellers in your segment
- Quick-pick onboarding ("tell us 3 things you like")
- Session-based recs after 1–3 clicks
New product
Tactics that work
- Content-based features (category, brand, attributes)
- Boosted exposure to the most similar audiences
- Capped promotion budget for organic signal
- Quick decay if engagement does not arrive
04Code — a simple ranking loop
ranker
def serve_homepage(user):
candidates = get_candidates(user, k=300)
feats = build_features(user, candidates)
scores = ranker.predict(feats)
ranked = sorted(zip(candidates, scores), key=lambda x: -x[1])
diversified = mmr(ranked, lambda_=0.4, k=24)
return apply_business_rules(diversified)05Measure the right thing
- Online: conversion rate, AOV, revenue per visitor, click-through on rec carousels
- Offline: NDCG, recall at K, A/B win rate on conversion
- Beware: pure CTR is gamed by clickbait categories like accessories
06The biggest pitfall
Quick Quiz
Your recommender increases CTR 18% but conversion rate is flat. What is most likely happening?
07
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See our retail workThe takeaway
Personalization is a stack — candidates, ranker, diversification, rules. Train on the metric you sell, not the metric that is easiest.
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