How TasteRay Works
Methodology, data sources, and the research behind our claims.
TasteRay is built on a different premise than mainstream streaming recommendations: we optimize for the movies and TV series you'll remember, not the ones that maximize watch time. This page documents how that works in practice.
The 92% hit rate
We claim 92% of users discover a new personal favorite within their first month. Here's how that's measured: in opt-in user research between 2025-09 and 2026-04, we asked users to rate each TasteRay-recommended title they watched on a 1–10 scale. A "personal favorite" is defined as any title rated ≥ 8/10 by the user. The metric counts the share of monthly active users with at least one ≥8/10 rating during the period. Sample size: 4,200+ active users. We refresh this number quarterly.
Cinephile-grade AI
Our recommendation engine is trained on more than 500,000 long-form reviews from professional critics, festival programmers, and verified cinephiles — not just star ratings or watch-history. The model learns which films map onto which emotional and aesthetic registers (e.g. "slow contemplative reflection" vs "kinetic catharsis"), and matches them to the user's stated mood, occasion, and prior loves.
Evaluation: held-out user studies measure recommendation quality against three baselines (Netflix's algorithm, IMDb top-rated by genre, and a random control). TasteRay's average user-rated quality (1–10) currently exceeds the next-best baseline by 1.8 points.
Citations and source research
Several landing pages reference psychological or behavioral research. The full bibliography:
- Dutton, D. G., & Aron, A. P. (1974). Some evidence for heightened sexual attraction under conditions of high anxiety. Journal of Personality and Social Psychology, 30(4), 510–517 — basis for the "misattribution of arousal" claim on the horror-for-date-night landing.
- Greenwood, D. N., & Long, C. R. (2009). Mood specific media use and emotion regulation. Media Psychology, 12(2), 165–186 — basis for our mood-based recommendation framing.
- Anderson, C. A., et al. (2003). The influence of media violence on youth. Psychological Science in the Public Interest, 4(3), 81–110 — context on content-rating conservatism for family pages.
Data sources
- The Movie Database (TMDB) — title metadata, posters, cast, crew, runtime, certification, streaming availability.
- Critic and audience reviews — long-form reviews aggregated under fair-use research provisions; not redistributed.
- User-provided signal — ratings, mood/occasion descriptors, and free-text feedback from TasteRay users.
- Letterboxd public lists and tags — for community-curated taste signals.
Last updated: 2026-05-10.