Content Recommendations
“What should this subscriber watch next?”
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A real-world example
What should this subscriber watch next?
Streaming platforms lose $1.5B annually to subscriber churn driven by poor discovery. When users can't find content they enjoy within 60-90 seconds, they disengage. Collaborative filtering alone misses the content graph: genre adjacencies, creator networks, and viewing context (time of day, device, co-viewers). For a platform with 50M subscribers, a 2% engagement lift from better recommendations prevents 200K cancellations worth $24M per year.
How KumoRFM solves this
Graph-powered intelligence for media platforms
Kumo connects subscribers, content, watch history, ratings, and genres into a unified graph. The GNN learns nuanced preferences: not just 'users who watched X also watched Y,' but 'users who watched X on mobile at night in genre cluster Z tend to engage with Y-type content within 48 hours.' PQL's RANK TOP operator delivers a ranked watchlist per subscriber, updated continuously.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
SUBSCRIBERS
| subscriber_id | plan | signup_date | preferred_device |
|---|---|---|---|
| SUB001 | Premium | 2023-08-15 | Smart TV |
| SUB002 | Standard | 2024-02-20 | Mobile |
| SUB003 | Premium | 2024-06-10 | Tablet |
CONTENT
| content_id | title | genre | release_year |
|---|---|---|---|
| MOV101 | Neon Heist | Action/Thriller | 2025 |
| MOV102 | Quiet Garden | Drama | 2024 |
| SER201 | Code Black | Sci-Fi/Drama | 2025 |
WATCH_HISTORY
| watch_id | subscriber_id | content_id | pct_watched | timestamp |
|---|---|---|---|---|
| W5001 | SUB001 | MOV101 | 95% | 2025-02-28 |
| W5002 | SUB001 | SER201 | 40% | 2025-03-01 |
| W5003 | SUB002 | MOV102 | 100% | 2025-03-01 |
RATINGS
| rating_id | subscriber_id | content_id | score |
|---|---|---|---|
| R301 | SUB001 | MOV101 | 4.5 |
| R302 | SUB002 | MOV102 | 5.0 |
| R303 | SUB003 | SER201 | 3.8 |
GENRES
| genre_id | name | parent_genre | avg_completion_rate |
|---|---|---|---|
| G01 | Action/Thriller | Action | 72% |
| G02 | Drama | Drama | 81% |
| G03 | Sci-Fi/Drama | Sci-Fi | 65% |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(WATCH_HISTORY.watch_id, 0, 7, days) FOR EACH SUBSCRIBERS.subscriber_id, CONTENT.content_id RANK TOP 10
Prediction output
Every entity gets a score, updated continuously
| SUBSCRIBER_ID | CONTENT_ID | TITLE | WATCH_PROB | RANK |
|---|---|---|---|---|
| SUB001 | MOV305 | Steel Rain | 0.78 | 1 |
| SUB001 | SER202 | Deep Signal | 0.65 | 2 |
| SUB002 | MOV102 | Quiet Garden | 0.71 | 1 |
| SUB002 | SER201 | Code Black | 0.52 | 2 |
Understand why
Every prediction includes feature attributions — no black boxes
Subscriber SUB001 -- Content MOV305 (Steel Rain)
Predicted: 78% watch probability (Rank #1)
Top contributing features
Genre overlap with high-rated content
Action/Thriller
30% attribution
Similar subscribers' completion rate
88%
25% attribution
Director overlap with watched titles
Same director
20% attribution
Time-of-day viewing pattern match
Evening
14% attribution
Content freshness (release recency)
2 weeks old
11% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: A streaming platform with 50M subscribers prevents 200K cancellations worth $24M per year by improving content discovery. Kumo's graph captures viewing context, genre relationships, and social signals that collaborative filtering alone misses.
Related use cases
Explore more media & entertainment use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
