Calculator Inputs
Enter recommendation overlap, business uplift, and discovery metrics. The calculator scores each component, normalizes the weights, and returns one composite personalization index.
Example Data Table
This sample shows how different recommender systems can produce different personalization profiles using the same evaluation framework.
| System | Users | List Size | Avg Overlap | Baseline CTR | Personalized CTR | Coverage | Novelty | Diversity |
|---|---|---|---|---|---|---|---|---|
| Streaming Feed Model | 120 | 10 | 3.0 | 4.2% | 6.1% | 42% | 68% | 74% |
| Retail Product Ranker | 250 | 12 | 4.4 | 3.8% | 5.3% | 55% | 61% | 66% |
| Learning Content Recommender | 90 | 8 | 1.9 | 5.1% | 7.2% | 47% | 79% | 81% |
Formula Used
The calculator uses a practical composite scoring model for personalization quality in recommendation systems.
1) Overlap Rate
Overlap Rate = Average Shared Items / Average Recommendation List Size
2) Uniqueness Score
Uniqueness Score = 1 − Overlap Rate
3) CTR Uplift
CTR Uplift % = ((Personalized CTR − Baseline CTR) / Baseline CTR) × 100
4) Conversion Uplift
Conversion Uplift % = ((Personalized CVR − Baseline CVR) / Baseline CVR) × 100
5) Uplift Component Scores
Uplift Score = Clamp(Uplift % / Uplift Cap, 0, 1)
6) Freshness Score
Freshness Score = 1 − Repeat Exposure Rate
7) Adjusted Novelty Score
Adjusted Novelty = (Novelty Score + Freshness Score) / 2
8) Final Personalization Index
PI = Σ(Normalized Weight × Component Score)
All percentage-based component scores are converted to a 0 to 1 scale before weighting. The final result is multiplied by 100.
How to Use This Calculator
- Enter the number of users included in your evaluation sample.
- Provide average recommendation list size and average item overlap between user pairs.
- Add baseline and personalized CTR plus conversion rates from your experiment.
- Enter catalog coverage, novelty, diversity, and repeat exposure percentages.
- Set an uplift cap that represents full credit for business lift.
- Adjust the weights to match your organization’s evaluation priorities.
- Click the calculate button to show the result above the form.
- Use the CSV or PDF buttons to save the generated result summary.
Frequently Asked Questions
1. What does the personalization index measure?
It measures how well a recommender separates users while also improving business outcomes and discovery quality. A higher score suggests better uniqueness, stronger uplift, broader coverage, and healthier novelty.
2. Why is overlap important?
If many users receive nearly identical lists, the system behaves more like a generic ranker than a personalized one. Lower overlap usually means better user-level differentiation.
3. Why include CTR and conversion uplift?
Personalization should not only look different across users. It should also perform better. CTR and conversion uplift connect ranking quality to measurable product outcomes.
4. What is adjusted novelty?
Adjusted novelty combines novelty with freshness. A model can recommend obscure items, but if it repeats them too often, the user experience still becomes stale.
5. Why are weights normalized?
Normalization keeps the final score stable regardless of the raw weight totals. You can change priorities without manually forcing the weights to add up to 100.
6. What does the uplift cap do?
It defines how much uplift earns a full component score. For example, if the cap is 50, then a 50% uplift or more maps to the maximum score for that metric.
7. Can this be used for A/B tests?
Yes. It works well for experiments comparing a baseline model with a personalized model. Use test-period values for CTR, conversion, and recommendation behavior.
8. Is there one universal best score?
No. Strong scores vary by product, traffic mix, catalog size, and business goals. The index is most useful for comparing models consistently over time.