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Feed Ranking Algorithm Design Template
A structured template for designing feed ranking algorithms. Covers scoring models, signal weighting, diversity rules, recency decay, abuse prevention,...
Updated 2026-03-05
Feed Ranking Algorithm Design
| # | Item | Value (1-10) | Effort (1-10) | Score | Priority | Owner | |
|---|---|---|---|---|---|---|---|
| 1 | 3.0 | ||||||
| 2 | 2.5 | ||||||
| 3 | 1.8 | ||||||
| 4 | 1.2 | ||||||
| 5 | 1.1 |
#1
3.0
#2
2.5
#3
1.8
#4
1.2
#5
1.1
Edit the values above to try it with your own data. Your changes are saved locally.
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Frequently Asked Questions
Should I start with chronological or algorithmic ranking?+
Start with chronological if your feed has fewer than 20 items per day per user. At that volume, users can scan everything, and algorithmic ranking adds confusion without benefit. Switch to algorithmic ranking when daily volume exceeds 20-30 items and users start missing important content. The transition should be gradual: offer a "Recent" toggle alongside the ranked view.
How do I handle the cold-start problem for new items?+
Give new items a time-limited ranking boost (e.g., 2x score for the first 6 hours) to ensure they get enough impressions for the algorithm to gather engagement signals. Without this boost, new items from less popular creators never get seen and therefore never accumulate the engagement data needed to rank well.
What is the right balance between relevance and recency?+
This depends on your feed type. For work feeds, recency should be weighted at 15-25% because actionable items have deadlines. For social feeds, relevance can dominate (40-50%) because the best content from yesterday is still worth seeing today. For news feeds, recency should be 30-40% because information value degrades quickly. Tracking [analytics metrics](/templates) on engagement-by-item-age helps you calibrate.
How do I measure if the feed ranking is "good"?+
Combine quantitative and qualitative signals. Quantitative: engagement rate, time-to-action, content breadth, DAU retention. Qualitative: user satisfaction surveys, support tickets about missing content, session replays. The strongest signal is D7/D28 retention: if users keep coming back, the feed is delivering value.
How often should I retrain a feed ranking model?+
For rule-based or linear scoring, update weights monthly based on engagement data review. For ML models, retrain weekly or daily depending on data volume and feature freshness. Monitor model performance drift with a control group. If the model's live metrics drop below the control group, something has changed in user behavior and the model needs retraining. ---
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