Content Personalization
“For each user, what content categories will they engage with in the next 30 days?”
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A real-world example
For each user, what content categories will they engage with in the next 30 days?
Content platforms show the same trending articles to everyone or rely on simple tag-based matching. Users who read technology articles about AI infrastructure are shown generic tech news instead of the specific sub-topics they care about. Engagement drops, session duration shrinks, and ad revenue follows. For a major publisher with 50M monthly users, a 10% improvement in engagement translates to $8-12M in additional annual ad revenue.
How KumoRFM solves this
Relational intelligence for true personalization
Kumo models the full user-content-interaction graph — reading time, scroll depth, sharing behavior, and cross-user patterns — to predict which content categories each user will engage with next. It discovers that users who read AI infrastructure pieces also engage deeply with cloud architecture content, a connection invisible to tag-based systems that treat categories as independent.
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
USERS
| user_id | age_group | signup_date |
|---|---|---|
| U001 | 25-34 | 2024-03-10 |
| U002 | 35-44 | 2023-09-22 |
| U003 | 18-24 | 2024-08-15 |
ARTICLE_VIEWS
| view_id | user_id | article_id | category | read_sec | timestamp |
|---|---|---|---|---|---|
| V001 | U001 | ART501 | Technology | 245 | 2025-02-18 |
| V002 | U001 | ART302 | Sports | 180 | 2025-02-19 |
| V003 | U002 | ART718 | Finance | 312 | 2025-02-18 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT LIST_DISTINCT(ARTICLE_VIEWS.CATEGORY, 0, 30, days) FOR EACH USERS.USER_ID
Prediction output
Every entity gets a score, updated continuously
| USER_ID | CLASS | SCORE | TIMESTAMP |
|---|---|---|---|
| U001 | Technology | 0.93 | 2025-03-12 |
| U001 | Sports | 0.81 | 2025-03-12 |
| U002 | Finance | 0.88 | 2025-03-12 |
Understand why
Every prediction includes feature attributions — no black boxes
User U001 (age 25-34, signed up 2024-03-10)
Predicted: Will engage with Technology content — score 0.93
Top contributing features
Technology articles read (30 days)
18 articles, avg 4.1 min
36% attribution
Graph neighbors' top category
82% also read Technology
25% attribution
Cross-category signal (Sports + Tech)
Sports-tech crossover pattern
18% attribution
Session depth (Technology)
3.2 articles per session
13% attribution
Share rate (Technology)
0.15 (3x platform avg)
8% 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: 2-4x engagement lift over rule-based personalization. For publishers with 50M+ monthly users, this translates to $8-12M in additional annual ad revenue.
Related use cases
Explore more personalization 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.
