Why architecture matters here
Recommenders fail on filter bubbles, cold start, and feedback loop bias. Architecture matters because each layer addresses a different failure mode.
The architecture: every piece explained
The top strip is the two-stage. User context features. Retrieval narrows corpus with fast index. Ranking model scores. Diversity + fairness shape final list.
The middle row is safeguards. Cold start uses content + collaborative signals. Exploration — epsilon-greedy — keeps discovery. Feedback logging impressions + clicks. Retraining keeps model fresh.
The lower rows are ops. Serving cache for hot. Metrics CTR + retention. Ops A/B + governance + cost.
End-to-end flow
End-to-end: user opens app. Retrieval fetches 500 candidates from vector index. Ranking model scores. Diversity constraint ensures at most 3 from one category. Cold-start item boosted via exploration. Impressions logged. Retraining nightly using yesterday's feedback. Metrics show CTR +8% + retention +2%.