Recommendation Engine

How it works

1. Viewer interactions are captured in real time — watch events, completion rates, search queries, ratings, skips, and session patterns sent to the data pipeline.
2. The behaviour data pipeline processes and stores interaction events — building and continuously updating per-viewer preference profiles.
3. The recommendation engine runs candidate generation — collaborative filtering identifies similar viewer cohorts, content-based filtering matches content attributes to viewer preferences.
4. Candidate titles are scored and ranked by predicted engagement probability — factoring in recency, catalog freshness, business rules, and real-time session context.
5. The ranking layer applies filters — removing already-watched content, enforcing entitlement rules, and applying editorial overrides for promoted titles.
6. Ranked recommendation lists are served to the platform via API — consumed by home screen rails, end-of-episode prompts, and similar title surfaces.
7. Viewer interactions with recommendations feed back into the model — clicks, completions, and skips continuously refine prediction accuracy.

Key components

• Behaviour data pipeline — real-time event collection and storage for viewer interactions across all sessions and devices
• Collaborative filtering model — identifies viewer similarity clusters and generates recommendations from shared behaviour patterns
• Content-based filtering model — matches content metadata attributes (genre, mood, tone, theme, cast) to viewer preference profiles
• Hybrid model layer — combines collaborative and content-based outputs with configurable weighting per context
• Real-time session signal processor — updates recommendations within the current session based on what was just watched
• Cold start handler — fallback logic for new users and new content with no interaction history
• Recommendation API — serves ranked suggestion lists to frontend surfaces with low latency per request

Performance impact

• Higher session depth — relevant recommendations increase average titles watched per visit and total session duration
• Improved catalog depth consumption — engine surfaces content beyond obvious popular titles, extending perceived catalog value
• Reduced time-to-play — personalized rails reduce the time viewers spend searching before starting playback
• Lower churn — viewers who consistently find relevant content to watch have significantly lower cancellation rates
• Higher new release viewership — relevance-based surfacing of new titles to the right audience segments drives first-week engagement
• Cold start improvement — better onboarding recommendations increase early engagement and reduce early-stage churn

Common issues

• Poor metadata quality — incomplete genre, mood, and theme tagging limits content-based filtering accuracy regardless of algorithm sophistication
• No real-time session signals — batch-only processing means current session context has no influence on recommendations until the next day
• Missing negative signals — skipped recommendations not used as down-ranking signals cause the engine to repeatedly surface unwanted content
• Cold start not handled — new users see popularity-ranked content for too long before personalization activates, increasing early churn
• No feedback loop measurement — recommendation CTR and completion rate not tracked means engine quality cannot be measured or improved
• Overfitting to recent history — engine over-weights the last few sessions and stops surfacing content from viewer's broader preference profile