Recommendations That Understand
Your Cognitive State
The first patented multi-regime cognitive model for content recommendations. Track saturation, predict regime switches, and deliver the right content at the right cognitive moment.
The Problem
Current recommendation systems treat viewers as static profiles. The reality is far more complex.
Single-Vector Trap
Current systems compress your entire personality into one preference vector. But you're not one mood — you switch between intellectual cinema and lighthearted comedy.
Cognitive Saturation Blindness
After 4 intense dramas in a row, Netflix still recommends #5. No system tracks when you're cognitively saturated and ready for something different.
Filter Bubble Effect
Algorithms optimize for similarity, creating echo chambers. Viewers get trapped in repetitive content cycles, leading to platform fatigue and churn.
The $1M Problem
Netflix offered $1M for just 10% improvement in recommendations. Even marginal gains translate to billions in reduced churn and increased engagement.
The Technology
A six-stage pipeline that transforms raw viewing behavior into cognitively-aware, regime-switching recommendations.
Deep Content Analysis
Automated extraction of quantitative features: dramatic complexity, emotional intensity, visual expressiveness, and narrative pacing. No manual tagging required.
Multi-Regime User Model
Instead of one preference vector, we maintain multiple cognitive regimes — distinct viewing states discovered through automatic clustering of viewing history.
Dynamic Saturation Index
For each regime, we compute a real-time saturation score based on cumulative cognitive load — a function of content complexity, pacing, and viewing duration.
Regime Switch Prediction
Using Markov models enriched with saturation data, we predict when and which cognitive regime the viewer will transition to next.
Preference Cone Filtering
Candidates are selected within a geometric cone in feature space around the predicted regime vector. The cone angle adapts as more data accumulates.
Controlled Novelty & Feedback
A novelty slider adjusts the minimum distance from recently watched content. Real-time feedback (completions, abandonments) continuously refines the model.
Key Advantages
Each innovation directly addresses a failure mode in existing recommendation systems.
Multi-Regime Precision
Not one vector — multiple cognitive states captured simultaneously. The system knows you love both Tarkovsky and silly comedies.
Saturation Tracking
Real-time measurement of cognitive load per regime. Knows when you've had enough heavy drama before you do.
Predictive Switching
Markov-based prediction of the next cognitive state. Recommends content for where you're going, not where you've been.
Controlled Novelty
Adjustable "novelty slider" prevents filter bubbles while staying within predicted comfort zones. Fresh, not random.
Dislike Risk Minimization
Analyzes abandoned views and low ratings to build a negative signal model. Filters out likely-to-fail content before it reaches the screen.
Profile Stability
Inertia coefficient prevents accidental views from corrupting the model. A child using a parent's account won't destroy years of preferences.
How It Works
A continuous feedback loop that learns, predicts, and adapts in real time.
Watch Anything
The system analyzes every piece of content you interact with — watches, skips, pauses, abandonments. Each action is a cognitive signal.
Regimes Emerge
Automatic clustering reveals your distinct viewing modes. "Intellectual exploration", "emotional relaxation", "background viewing" — discovered, not assigned.
Saturation Builds
As you consume content in one regime, cognitive load accumulates. The saturation index rises. The system watches for the tipping point.
Perfect Switch
Before you even feel tired of heavy dramas, the system predicts your next state and serves the perfect contrast — matched to where your mind is heading.
Beyond Streaming
The multi-regime cognitive model is universally applicable across industries that manage attention and engagement.
Video Streaming
Personalized film and series recommendations with cognitive state awareness. The primary application.
Music Platforms
Dynamic queue management based on tempo-rhythmic saturation and auditory fatigue patterns.
EdTech
Adaptive learning difficulty management, preventing student burnout through cognitive load balancing.
E-Commerce
Product recommendations based on shopping mode transitions — from functional to emotional purchases.
Gaming
Dynamic difficulty adjustment and content pacing based on real-time player engagement regimes.
License This Technology
Interested in integrating multi-regime cognitive recommendations into your platform? Reach out to discuss licensing, acquisition, or partnership opportunities.