Design LinkedIn Learning course recommendations

Q: Design LinkedIn Learning course recommendations

The question evaluates proficiency in end-to-end machine learning system design, covering feature engineering from job post text, candidate generation and ranking, model training and labeling, evaluation metrics, and production concerns such as cold start, bias, and feedback loops.

Q: How do I approach ML System Design interview questions?

ML System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master ml system design interviews.

Question

Design a mini ML system to recommend LinkedIn Learning courses to a user.

Product goal:

Recommend courses that help the user succeed in their job search and/or current role.

Available signals (examples):

Jobs the user applied to, jobs the user viewed/saved, jobs recommended to the user.
User profile (title, skills, seniority, industry), past course consumption, dwell/completion, search queries.
Course metadata (title, description, skills taught, difficulty, duration).
Job post text (description, requirements).

Requirements:

Propose an end-to-end architecture (data collection → feature generation → candidate generation → ranking → serving).
Explicitly describe how you would extract useful features from job post text (e.g., skills/requirements). Mention at least one non-LLM and one LLM-based approach.
Discuss training data/labels, offline and online evaluation metrics, and key failure modes (cold start, bias, feedback loops).
Briefly describe how you would incorporate business constraints (freshness, diversity, exploration, compliance).

Design LinkedIn Learning course recommendations

Quick Overview

Solution

Comments (0)