A recommendation system with product thinking inside.
Translating a personalized learning brief into user stories, requirements, and technical trade-offs.
01 / THE PROBLEM
What needed to make more sense?
An education platform needs to recommend relevant courses based on interests, skills, and career goals. The product has to serve learners, instructors, and admins while managing performance, privacy, and a cold-start problem.
02 / DISCOVERY
How I approached it
- Wrote separate user stories for learners, instructors/content creators, and platform admins.
- Defined functional priorities including recommendations, skill assessment, learning progress, analytics, and engine management.
- Specified non-functional requirements for performance, scalability, uptime, security, monitoring, compliance, and extensibility.
03 / PRODUCT DECISIONS
The proposed direction
- Start with a lightweight onboarding quiz of up to five questions, taking less than two minutes.
- Use career goals, topic interests, skill level, and relevant learning activity to shape the recommendation experience.
- Support creator analytics and admin monitoring alongside learner-facing recommendations.
- Collect only necessary learning data, give privacy controls, secure storage, and explain data use.
04 / MEASURES & LIMITS
What success would look like.
All business figures are assignment objectives, not measured outcomes. The brief also targets a 20% satisfaction improvement. The document lays out system components, trade-offs, assumptions, and possible future extensions.
05 / THE TAKEAWAY
The thought I’d carry forward.
The cold-start experience and privacy requirements should influence the first version of the system. Basic declared interests can be useful before a detailed behavioral profile exists.
06 / ALL THE DETAILS
The original work, in full.
The complete research, diagrams, screens, and supporting details are in the original document.
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