Adobe ML Engineer: Recommendation Systems (Fundamentals)

What the question covers

You’ll be asked to demonstrate foundational knowledge of recommendation systems: types (collaborative, content-based, and hybrid), algorithmic approaches (matrix factorization / SVD, alternating least squares, neural recommenders, and sequence-aware models), and practical deployment challenges like cold start, sparsity, scalability, and bias.

Typical flow in the interview

First, you’ll define and compare approaches (when to use collaborative vs content-based). Next, interviewers often ask you to explain the math behind matrix factorization (objective, regularization, and ALS updates) or to sketch a neural/session-based model for sequences. Expect follow-ups on cold-start solutions, implicit vs explicit feedback, and how you’d evaluate models offline (precision@k, recall@k, NDCG, MAP) and online (A/B testing).

What you need to demonstrate

You should show: clear intuition for algorithms, ability to derive or reason about loss functions and update rules, familiarity with evaluation metrics and their trade-offs, strategies for cold-start (side features, meta-learning, hybrid systems), and production considerations (scaling with ALS/SGD, approximate nearest neighbors, latency vs accuracy trade-offs). Also be prepared to discuss fairness and bias mitigation.

Use concise examples (one-sentence math derivations or pseudocode) and articulate trade-offs; interviewers value clear reasoning and practical solutions as much as theoretical depth.