Module 2: Recommendation systems

Theme

Recommendation systems

Essential Question

How do recommendations balance relevance, diversity, and business goals?

Module Components

• Book prose: conceptual framing, domain scenario, methods, and failure modes

• Assignment: evidence-backed production of a specific artifact

• Slides: presentation sequence for seminar or lecture delivery

• Narration: spoken version of the slide flow

• Rubric: criteria for evaluating the module artifact

• Notebook: executable lab aligned with the module theme using synthetic customer records with engagement, recency, spend, channel preference, and consent state

Module Artifact

customer insights package with segmentation, measurement design, and consent constraints focused on recommendation systems: Prototype or specify a recommender.

Professional Setting

Students work as if advising a growth team deciding how AI-generated customer insights should guide campaign targeting. Their work must be intelligible to marketing lead, analytics manager, privacy counsel, and customer experience owner.

Use This Module in Order

1. Read the learning chapter.

2. Review the slide deck with the matching narration.

3. In Populi, open the private student-repository link for this course and enter modules/module-2.

4. Clone the repository once or open its Codespace/Colab copy; run lab.ipynb and complete exercise.ipynb there.

5. Self-check with the rubric, commit and push the work, then submit exactly what Populi requests.