A recommendation engine can chase clicks by narrowing every feed toward what a shopper already likes, or it can keep the catalog broad and let discovery happen. Neither extreme is obviously right — it depends on how much a business values short-term engagement against long-term catalog reach.
The AI Personalization Lab models 2,000 shoppers browsing a 40-category catalog. Sliding personalization aggressiveness up concentrates recommendations around each shopper's inferred favorite category, lifting the modeled click-through rate while shrinking the number of categories that ever get meaningful exposure.
That shrinking exposure is the filter-bubble effect in miniature: categories that never get surfaced can't build the engagement signal they'd need to earn more exposure later, a self-reinforcing narrowing that pure short-term optimization won't catch on its own.
🧪 Try it yourself: the AI Personalization Lab simulation lets you slide personalization aggressiveness and watch the category-diversity outcome update live.
🧪 Try it yourself: the AI Personalization Lab simulation lets you experiment with everything described above directly in your browser.