How to Build a Recommendation System with AI in 2026 (Step-by-Step Guide)

Quick Answer

Combine content embeddings (for cold start) with collaborative filtering (for warm users), then re-rank with an LLM for final personalization. Store vectors in pgvector, use Supabase real-time for events. Works for products, articles, videos, or users.

• Time to v1: 1-2 weeks
• Cost: $50-200/mo at small scale
• Expected lift: 10-40% on engagement metrics

What You'll Need

• Supabase with pgvector
• Item catalog (products, articles, videos)
• User event log (views, clicks, purchases)
• Embedding API

Steps

1. Embed every item. For products: title + description + category. For articles: title + first paragraph. Store in items.embedding.
2. Build content-based recs (cold start). For a given item, find nearest neighbors by cosine similarity. SELECT * FROM items ORDER BY embedding <=> target_embedding LIMIT 10.
3. Log user events. events (user_id, item_id, event_type, created_at). Event types: view, click, add_to_cart, purchase.
4. Build user embeddings. Average embeddings of items user engaged with, weighted by event type (purchase=3x, click=1x). Update on each event.
5. Collaborative layer. For similar users: SELECT user_id FROM user_embeddings ORDER BY embedding <=> target_user LIMIT 50. Recommend items they engaged with that target user hasn't seen.
6. LLM re-rank (optional, for premium experience). Pass top 20 candidates + user context to LLM: "Re-order these for a user who [preferences]. Return top 10 IDs." Cache per user per day.
7. Diversity & exploration. Don't show 10 identical products. Apply MMR (maximal marginal relevance) or just boost items from different categories. 10% random for exploration.
8. Measure lift. A/B test recs vs chronological/popular. Track CTR, conversion, session time.

Common Mistakes

• Only showing similar items: Users want "you might also like" PLUS "something new." Balance exploit/explore.
• No time decay: 2-year-old clicks shouldn't weigh as much as yesterday's. Decay weights exponentially.
• Recommending out-of-stock: Filter by availability always.
• Bias toward popular: Popularity ≠ personal. Normalize for item popularity.
• No fallback: Cold-start user with zero events still needs recs. Show editorial + category best-sellers.

Top Tools

Tool	Best For	Price
Supabase pgvector	Vector store + SQL	Free tier
Recombee	Hosted recs	Paid
LightFM	Hybrid model	Free
Qdrant	Alternative vector DB	Free tier
PostHog	Event tracking	Free tier

FAQs

Q: Do I need a dedicated recommender system like Recombee? Only at 100K+ users. Below that, pgvector + SQL beats hosted.

Q: Matrix factorization vs embeddings? Embeddings win for cold-start & multi-modal (text + image). MF still strong for warm users.

Q: How often to retrain? Content embeddings: when catalog changes. User embeddings: on every event (streaming).

Q: Can I explain recommendations? Yes — "because you viewed X" uses nearest-item attribution. Builds trust.

Q: Privacy concerns? Don't leak other users' data via recs. Apply k-anonymity thresholds.

Q: Do I need GPU? No for inference (pgvector is CPU). Yes for training custom models.

Conclusion

Recommendations compound revenue. Start with simple content-based, add collaborative as you gather events, layer LLM re-ranking for premium feel. Ship fast, measure lift, iterate monthly.