TasteTwin taste-matched picks

Restaurant and recipe recommendations from people whose taste matches yours, not the global average

Problem

A 4.6 on Google Maps is an average of everyone and often doesn't match your own palate; spice lovers and picky eaters keep getting burned by ratings and influencer lists. People want to know what diners with similar taste love, but platforms only offer aggregate scores and ad slots.

Solution

Users rate a handful of dishes, the app finds their "taste twins" and recommends restaurants and recipes those twins rated highly but the user hasn't tried. Cold start is light — a few ratings produce results — and every recommendation shows the reasoning via quotes from similar tasters.

Users

Urban young adults who eat out often, cook at home and have distinct taste preferences

MVP

Fast onboarding rating a few dozen common dishes/cuisines; simple taste vector plus nearest-neighbor matching; recommendation feed with reasons and quotes; save and want-to-try list; invite friends to compare taste

Revenue

Free core plus subscription for deeper taste profiles and cross-city picks; local restaurant affiliate commissions or curated list partnerships

Why now

Recommendation building blocks and public dish/recipe data make cold start feasible; big platforms optimize for global ratings, so vertical taste matching is a gap one person can ship in weeks