AI-curated product recommendations: how they’re made (and how to judge them)

Opening scene
You open a shopping app, and somehow — between browsing sneakers and a blender — it knows you’re about to move into a new apartment. Your “recommended for you” list looks uncannily accurate. It’s not coincidence; it’s correlation, built by AI.

How recommendations are made
AI systems analyze multiple layers of data: product metadata, visual attributes, reviews, and user behavior. According to Statista, 80% of online shoppers say they’ve purchased something suggested by an algorithm. These engines learn from collective behavior, predicting what similar users might want next.

The invisible criteria
Deloitte explains that modern recommendation models don’t just guess. They calculate proximity between products — for example, people who buy “A” often view “B,” and so on. Layered with contextual factors like price range, brand loyalty, and time of day, the system crafts individualized suggestions that feel human.

What to watch for
The line between helpful and manipulative can blur. A 2023 Pew Research report found that 56% of users worry about “hidden bias” in AI-driven content. Transparency reports from companies like Amazon and Netflix show progress — but consumers still deserve clarity.

Where Marty fits in
Marty curates results based on data you control — what you type, browse, or refine. It compares items across stores and filters out redundant listings so you can focus on real value. Instead of trying to predict every whim, Marty surfaces what matches your stated needs and priorities.

🔗 Explore how Marty curates your perfect picks → heymarty.com

FAQs
1. What kind of data trains these recommendations?
Anonymized behavioral data, product specs, and aggregated user trends.

2. Can recommendations be wrong?
Yes — algorithms reflect past patterns, which means unusual preferences may require refinement.

3. How is bias reduced?
By training on diverse, verified data sources and constant evaluation.

4. Are AI recommendations replacing human choice?
No — they’re meant to simplify, not dictate.

Sources: Statista (2024), Deloitte (2024), Pew Research (2023), McKinsey (2024).