Contextual ranking or collaborative recommendations?

Use contextual ranking when session/device/location matter more; use collaborative filtering when you have enough history and want to exploit user/item similarities.

How do I measure impact?

Run A/B tests; track engagement/conversion metrics and segments; monitor drift, bias and content saturation.

Apply Responsible AI principles: transparency, consent, opt‑out, limits for sensitive categories and auditing for automated decisions.

Decision on Azure: Recommendations, Ranking & Decision Support

Services & patterns

Personalization & Ranking

Order actions/content based on context (session, device, recent history) with implicit/explicit feedback.

Recommender systems

Collaborative/content‑based, sequential or hybrid for e‑commerce, media, training and knowledge.

Anomaly & Metrics

Detect anomalies on KPIs/time series for campaigns, inventory and quality; trigger alerts and remediation.

Policy & rules

Combine ML with rules (compliance, budgets, priorities) to deliver explainable, controllable decisions.

Reference integration

APIs & Feature Store

Expose REST/SDK endpoints; maintain a feature store (users, items, signals) with near real‑time updates.

POST /ranking/v1/rank
Ocp-Apim-Subscription-Key: <key>
Content-Type: application/json

Feedback loop & A/B

Collect rewards/clicks, exposure logs, A/B experiments and targeting; pipelines with Azure ML, Functions and Storage.

Quick comparison

Capability	When to use	Output
Contextual ranking	In‑session choices (hero/banner/actions)	Ordered list + score
Recommendations	Personalizing catalogs/items	Top‑N items + confidence
Anomaly	Monitoring KPIs & campaigns	Anomaly signals + severity
Policy engine	Constraints/overrides & compliance	Reasoned decision + logs

Best practices

Data & features

Avoid information loss, define stable IDs, manage cold‑start and update delays.

Continuous evaluation

CTR/CR, NDCG, MAP; separate offline vs online; check bias and offer saturation.

Governance

Responsible AI, explainability, limits for sensitive categories, auditing and safe rollbacks.

Next steps

Explore decision use cases Start with AI‑900 Advance with AI‑102

FAQ

How do I handle cold‑start?

Use popularity, editorial rules, content similarity and progressive profiling to bridge missing history.

Online learning or batch?

Batch for stability/costs; online/near‑real‑time when context changes fast and feedback is abundant.

How to ensure fairness & transparency?

Exposure logs, category limits, fairness metrics and decision explanations where possible.