Sample Answer
I’d first quantify the problem: measure item-level and category-level entropy, average catalog coverage per user, and session-level dwell time. If CTR is +5% but catalog coverage dropped 30% and session time is -7%, it confirms over-specialization. I’d run offline simulations and online A/B tests with re-ranking. Concretely, I’d add a diversity term to the scoring function (e.g., MMR or xQuAD) using item embeddings from a matrix factorization or two-tower model, tuning a lambda parameter to trade off predicted relevance vs similarity. I’d also cap same-brand/category impressions per session. Success metrics: maintain ≥3–4% CTR uplift while improving catalog coverage by 15–20% and recovering session duration to within 1–2% of baseline.
Keywords
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