Should I use embeddings for product recommendation?

Yes—embeddings can be a very good choice for product recommendation, but usually as part of a hybrid recommender, not the only method.

When embeddings work well

Use embeddings if you want to recommend products based on:

• Similarity of items
  e.g. “customers who liked this also liked these similar items”
• Text-rich products
  titles, descriptions, reviews, attributes, images
• Cold-start products
  new items with little or no interaction history
• Semantic matching
  e.g. “running shoes for flat feet” matching relevant products even if exact keywords differ
• Large catalogs
  embeddings + vector search scale well for candidate retrieval

Typical ways to use embeddings

You can build embeddings from:

• Product content: title, description, category, brand, specs
• User behavior: clicks, purchases, dwell time, adds to cart
• Both: user embeddings and item embeddings learned jointly

Then use them for:

• Nearest-neighbor retrieval
• “Similar items” recommendations
• Personalized ranking features
• Search + recommendation blending

When embeddings may not be enough

Embeddings alone are often weaker if you need:

• Explainable recommendations
• Strong business-rule control
• Real-time session awareness
• Highly personalized ranking based on complex patterns
• Accuracy from lots of structured interaction data

In those cases, you’ll often want:

• collaborative filtering / matrix factorization
• gradient-boosted ranking models
• sequence/session recommenders
• rules and filters on top

Practical recommendation

A common setup is:

1. Use embeddings for candidate generation
2. Use a ranking model to reorder results
3. Add business logic like availability, margin, category diversity, or exclusions

This gives you:

• good recall
• scalable retrieval
• better personalization
• more control

Rule of thumb

• Small catalog + simple needs: embeddings may be overkill
• Large catalog or content-heavy catalog: embeddings are often a strong fit
• Need best overall performance: use embeddings in a hybrid system

If you want, I can help you decide based on your catalog size, data available, and recommendation goal.