A recommendation engine that only shows proven, high-click items maximizes short-term click-through rate but leaves brand-new catalog items permanently starved of exposure. This is the classic cold-start problem, and an epsilon-greedy policy addresses it by setting aside some recommendation slots purely for exploring less-proven items.
The AI Recommendation Engine Lab models a 1,500-item catalog, one-fifth of it brand new. Raising the exploration rate gives more new items a chance to be shown and build click data, at some cost to the overall click-through rate in the short run.
What makes this trade-off different from most is that today's exploration is what makes tomorrow's exploitation better — a catalog that never explores stays stuck with the same top items forever, since nothing new ever gets the data it needs to be considered proven.
🧪 Try it yourself: the AI Recommendation Engine Lab simulation lets you move the exploration rate and watch the weekly outcome update live.
🧪 Try it yourself: the AI Recommendation Engine Lab simulation lets you experiment with everything described above directly in your browser.