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Excited to share
@LinkedIn
's innovative approach to evaluating semantic search quality! As part of the Search AI team, we've developed a groundbreaking evaluation pipeline that revolutionizes how we measure search relevance.
>> Key Innovation: On-Topic Rate (OTR)
This novel metric measures the semantic match between queries and search results, going beyond simple keyword matching. The system evaluates whether content is truly relevant to the query's intent, not just matching surface-level terms.
>> Technical Implementation Details
Query Set Construction
• Golden Set: Contains curated top queries and complex topical queries
• Open Set: Includes trending queries and random production queries for diversity
Evaluation Pipeline Architecture
1. Query Processing:
- Retrieves top 10 documents per query
- Extracts post text and article information
- Processes both primary content and reshared materials
2. GAI Integration:
- Leverages GPT-3.5 with specialized prompts
- Produces three key outputs:
- Binary relevance decision
- Relevance score (0-1 range)
- Decision reasoning
Quality Assurance
• Validation achieved 94.5% accuracy on a test set of 600 query-post pairs
• Human evaluation showed 81.72% consistency with expert annotators
>> Business Impact
This system now serves as LinkedIn's benchmark for content search experiments, enabling:
• Weekly performance monitoring
• Rapid offline testing of new ML models
• Systematic identification of improvement opportunities
What are your thoughts on semantic search evaluation?
>> Key Innovation: On-Topic Rate (OTR)
This novel metric measures the semantic match between queries and search results, going beyond simple keyword matching. The system evaluates whether content is truly relevant to the query's intent, not just matching surface-level terms.
>> Technical Implementation Details
Query Set Construction
• Golden Set: Contains curated top queries and complex topical queries
• Open Set: Includes trending queries and random production queries for diversity
Evaluation Pipeline Architecture
1. Query Processing:
- Retrieves top 10 documents per query
- Extracts post text and article information
- Processes both primary content and reshared materials
2. GAI Integration:
- Leverages GPT-3.5 with specialized prompts
- Produces three key outputs:
- Binary relevance decision
- Relevance score (0-1 range)
- Decision reasoning
Quality Assurance
• Validation achieved 94.5% accuracy on a test set of 600 query-post pairs
• Human evaluation showed 81.72% consistency with expert annotators
>> Business Impact
This system now serves as LinkedIn's benchmark for content search experiments, enabling:
• Weekly performance monitoring
• Rapid offline testing of new ML models
• Systematic identification of improvement opportunities
What are your thoughts on semantic search evaluation?