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Case Study: Search and Listing Ranking System Design

End-to-end design of a search and listing ranking system — the pattern behind e-commerce search (Amazon, eBay), marketplace search (Airbnb listings), and enterprise search. Covers query understanding, hybrid retrieval (BM25 + semantic), learning-to-rank, personalization, position bias, and multi-objective ranking that balances relevance, quality, and revenue. Grounded in Khang Pham's search/listing ranking case study.

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Subtopics
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Projects
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Quiz Qs
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Flashcards
▶📚 Prerequisites(3)

🎓 Learning objectives

  • •Distinguish search ranking from recommendation: explain where they share patterns and where they diverge
  • •Design a query understanding pipeline covering tokenization, spelling correction, query expansion, and intent classification
  • •Explain BM25 retrieval and describe when semantic (embedding) retrieval outperforms it and vice versa
  • •Describe the three learning-to-rank paradigms (pointwise, pairwise, listwise) and explain when to use each
  • •Identify the key feature categories used in search ranking (query, document, query-document interaction, user context) and explain why interaction features carry the most signal
  • •Explain position bias in search and describe two approaches to correcting it (IPS and examination model)
  • •Design a multi-objective re-ranking policy that balances relevance with quality signals and business goals

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📂 Subtopics

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Query Understanding and Hybrid Retrieval

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Learning-to-Rank: Features, Models, and Position Bias

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Re-ranking, Multi-Objective Scoring, and Evaluation

Related concepts

BM25TF-IDFinverted indexlearning-to-rankLambdaRankLightGBM rankerNDCGMRRquery understandingquery expansionspell correctionsemantic retrievalhybrid retrievalRRFposition biasIPS correctionexamination modelfeature engineeringquery-document interactionrecommendation-system-componentscase-study-recommendation

Next to learn

case-study-ad-prediction →recommendation-system-components →eval-metrics-fundamentals →ai-system-architecture-patterns →
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