I'm frustrated with Amazon Personalize because the recommendations feel generic

That’s a common complaint with Amazon Personalize. When recommendations feel “generic,” it usually means the model isn’t getting enough strong signal, or it’s being constrained by setup choices.

A few likely reasons:

• Not enough user/item interaction data: If events are sparse, Personalize falls back to broad popularity patterns.
• Cold start problem: New users or new items often get generic recommendations until enough activity accumulates.
• Weak event quality: If clicks, views, purchases, and dwell time aren’t tracked well, the model can’t learn meaningful preferences.
• Overly broad filters/campaign setup: Business rules or filters may be removing too much personalization.
• Metadata isn’t rich enough: Item categories, genres, brands, price bands, etc. help make recommendations more specific.
• Training data is too old: If you’re not refreshing the solution often, the model may reflect stale behavior.

What usually helps:

1. Capture more interaction types
   Track views, clicks, add-to-cart, purchases, and ranks of interactions by importance.

2. Improve item metadata
   Add meaningful features like category hierarchy, attributes, and text fields.

3. Use event weights/recency Give recent and high-intent actions more weight.

4. Create better user context Include session behavior, device, location, or referrer if relevant.

5. Test different recipes Some Amazon Personalize recipes work better for personalization vs. popularity-style recommendations.

6. Evaluate against a baseline Check whether it’s truly generic, or just weaker than your expectations compared with popularity or co-occurrence baselines.

If you want, I can help you troubleshoot it systematically. For example, I can give you a checklist to diagnose why Personalize is producing generic recommendations or help you design a better event schema and metadata model.