RecExplain

RecExplain is a web app (with optional API) that helps product and data teams understand, debug, and improve recommendation systems by generating human-readable explanations and actionable diagnostics. You connect your event data and recommendation outputs (from your existing model or vendor), and RecExplain surfaces why items were recommended: top contributing signals, counterfactuals (what would need to change to recommend something else), and segment-level patterns. It flags common failure modes like popularity bias, filter bubbles, cold-start overfitting, and leakage-like correlations. The app also provides lightweight A/B experiment guardrails: pre-flight checks, metric selection (CTR vs. long-term value), and automated “what changed?” reports after a rollout. This is not a replacement recommender model; it’s a practical observability layer that makes existing recommenders less risky to ship and easier to iterate on, especially for teams without deep ML interpretability expertise.