Solving the 'Cold Start' Discovery Problem via Sentiment Analysis.
A "Zero-Party Data" prototype designed to solve the 'Cold Start' discovery problem.
Traditional personalization relies on deep user history. This Discovery Engine inverts the model by explicitly capturing user context ("Mood") to generate high-relevance recommendations immediately.
Built as a lightweight, dependency-free application, it demonstrates a Federated Search architecture: mapping abstract user sentiments to concrete metadata taxonomies and fetching real-time media assets via external REST APIs.
A single-file, no-framework web app that turns vibes into watch-and-read lists. Type a mood (“melancholy,” “epic,” “playful”) or pick from 50 supported moods, and the app instantly recommends 4 titles tailored to your mood across Comics, Movies, or TV via a sticky Media Dropbox.
Each pick shows a clean one-liner rationale, live cover thumbnails (auto-fetched from Wikipedia with local caching), and a details overlay with summary, tags, and a single “Where to Watch” pill for fast streaming lookups.
The interface is intentionally minimal: centered card, big input, Enter-to-submit, subtle animated “mood atmospheres,” and a scrollable mood rail with keyboard focus states.
Everything ships in one ≤60KB index.html, portable, fast, and easy to host.
