Trust Vector
Taste-Matched Recommendations · 7 demonstrations · Built for Marketplaces · Booking Platforms · Review Aggregators
A traditional rating system averages every review equally. Kornerway reads each reviewer's taste fingerprint, computes how closely it matches yours, and surfaces only the voices whose preferences actually predict yours. So instead of trusting the crowd, you trust the reviewers whose taste resembles your own — and the recommendations that follow are the ones most likely to fit.
These seven demonstrations walk through how it works — from interactive trust propagation through live AI review decoding to full network topology analysis. Demo data is from a restaurant marketplace; your deployment would work across hotels, spas, fine dining, or any vertical with structured attribute reviews.
Build your taste profile, watch the D3 trust network compute direct and indirect reviewer relevance in real time
Open →500 users, 40 reviewers, 80 restaurants. MAE, Pearson r, bootstrap CI — statistically significant head-to-head vs baselines
Open →Two-pass AI extracts dining context and adjusts raw attribute scores — showing how business dinners should differ from family outings
Open →Paste any review, watch structured attribute scores, context adjustments, and confidence levels extract live against the Kornerway API
Open →Trust network topology, reviewer clustering coefficients, edge distributions — how network structure affects cold-start
Open →Tune trust thresholds, network depth, attribute weights on synthetic cohorts — see accuracy vs coverage trade-offs in real time
Open →Pilot dashboard with real-time ingestion metrics, trust network health, and recommendation performance — white-label ready
Open →