Content Recommendation System Specification Template

Content-based filtering works well when you have rich metadata about your content (tags, categories, embeddings) and users with thin behavioral histories. Collaborative filtering shines when you have dense interaction data across many users. Most production systems use a hybrid: content-based for cold-start and candidate generation, collaborative for ranking once you have enough signals. Start with content-based and add collaborative signals as your user base grows.

Most content platforms see meaningful personalization after 5-15 interactions. Fewer than 5 gives the model too little signal. More than 15 means users sit in generic mode too long and may churn before recommendations improve. Track the inflection point where personalized recommendations outperform popularity-based ones in your A/B tests, and use that as your threshold.

Build diversity constraints into your re-ranking layer. Reserve 10-20% of recommendation slots for exploration content outside the user's primary interests. Track topic diversity as a guardrail metric and alert if it drops below a threshold. Give users controls to discover new topics. Periodically audit the system to ensure it is not concentrating engagement on a narrow set of content creators. For more on responsible AI product practices, see the [Responsible AI Framework](/frameworks/responsible-ai-framework).

It depends on content velocity. A news platform with hundreds of new articles daily should retrain daily or use online learning. A media library with slower content additions can retrain weekly. Monitor model freshness by tracking recommendation CTR over time after each retraining. If CTR degrades significantly within 48 hours, your retraining cadence is too slow.

No. Each surface has different user intent. A homepage feed serves browsing intent (show variety). An article sidebar serves "read next" intent (show related content). An email digest serves re-engagement intent (show the single best piece). Define the objective and signal weighting separately for each surface, even if they share the same underlying model. ---