Improve Keyword Search Ranking
Problem Statement
You are tasked with improving the search ranking for an existing product. Currently, the system relies on a keyword-based index, such as an inverted index with lexical matching, for candidate generation. While users can generally find relevant documents, the most pertinent results frequently appear low in the search rankings, leading to user dissatisfaction.
Objective
Design an enhanced search ranking system. The existing keyword index must be retained as the initial candidate generation layer, but the primary goal is to significantly improve the ordering and relevance of the search results.
Key Areas to Address
- Diagnosis: How would you identify the root causes for relevant results being ranked too low?
- Feature Engineering: What additional ranking features would you incorporate beyond simple keyword matching?
- Machine Learning Integration: How would you leverage machine learning techniques, if suitable, to boost relevance?
- Data Collection: Describe your strategy for collecting labels or feedback data to train and evaluate the system.
- System Serving: How would you design the system to serve results with low latency, especially given the new ranking logic?
- Evaluation Metrics: What offline and online metrics would you employ to measure the success and effectiveness of your improvements?
- Advanced Considerations: Discuss how you would address follow-up issues such as cold start, personalization, freshness, spam detection, and fairness in ranking.
AI interview prep
Core dilemma
Improving search relevance through complex ML models and rich features while maintaining strict low-latency serving requirements and seamlessly integrating with an existing keyword-based candidate generation system.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Improve Keyword Search Ranking
Problem Statement
You are tasked with improving the search ranking for an existing product. Currently, the system relies on a keyword-based index, such as an inverted index with lexical matching, for candidate generation. While users can generally find relevant documents, the most pertinent results frequently appear low in the search rankings, leading to user dissatisfaction.
Objective
Design an enhanced search ranking system. The existing keyword index must be retained as the initial candidate generation layer, but the primary goal is to significantly improve the ordering and relevance of the search results.
Key Areas to Address
- Diagnosis: How would you identify the root causes for relevant results being ranked too low?
- Feature Engineering: What additional ranking features would you incorporate beyond simple keyword matching?
- Machine Learning Integration: How would you leverage machine learning techniques, if suitable, to boost relevance?
- Data Collection: Describe your strategy for collecting labels or feedback data to train and evaluate the system.
- System Serving: How would you design the system to serve results with low latency, especially given the new ranking logic?
- Evaluation Metrics: What offline and online metrics would you employ to measure the success and effectiveness of your improvements?
- Advanced Considerations: Discuss how you would address follow-up issues such as cold start, personalization, freshness, spam detection, and fairness in ranking.