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Search Efficiency and Hit Rate Improvement
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Search Efficiency and Hit Rate Improvement

Owned by Mieko Hashimoto
Last updated: Apr 22, 2025

 

This document serves as our northstar for search efficiency and hit rate improvement. As of this writing, development efforts are instead centered on nearer-term goals from User Behavior Search Metrics & Data Collection (2025) , balancing long-term goal with practical feasibility.

The companion document, “Rethinking Search Success Metrics,” reflects on the pros and cons of the metrics listed herein.

 

This document outlines a practical approach for tracking and improving search efficiency and hit rate within a biomedical search interface. It defines key performance metrics, details methods for collecting relevant data, and offers a suggested action plan for optimizing query understanding, ranking algorithms, and user experience. The goal is to enable measurable progress toward a targeted improvement in search performance.

 

1. Key Metrics to Track Search Efficiency and Hit Rate

To measure progress toward an x% improvement in search efficiency and hit rate, the following metrics should be tracked:

Search Efficiency Metrics:

  1. Time to First Relevant Result (TFRR)

    • Definition: The average time it takes for a user to find a relevant result in the search interface.

    • Goal: Reduce TFRR by at least x%.

    • Data Collection: Log user interactions, scroll depth, and dwell time per result.

  2. Search Abandonment Rate

    • Definition: Percentage of searches where users do not click on any results.

    • Goal: Reduce abandonment by improving search relevance.

    • Data Collection: Track query-to-click conversion via logging.

  3. Click Position of First Relevant Result

    • Definition: The position of the first relevant result that a user clicks.

    • Goal: Improve ranking so that relevant results appear in the top 3 positions.

    • Data Collection: Analyze click logs and heatmaps.

Hit Rate Metrics (Improving Retrieval Relevance):

  1. Query Success Rate (QSR)

    • Definition: Percentage of queries that return at least one relevant result based on user engagement (clicks, dwell time).

    • Goal: Increase QSR by x% over the benchmark.

    • Data Collection: Analyze log data and explicit user feedback.

  2. Precision at K (P@K) & Recall

    • Definition: Measures how many of the top K results are relevant (precision) and how many relevant results are retrieved in total (recall).

    • Goal: Improve precision@5 and recall by x%.

    • Data Collection: Evaluate using a manually labeled dataset of query-result pairs.

  3. Mean Reciprocal Rank (MRR)

    • Definition: Measures the ranking quality of the first relevant search result.

    • Goal: Improve MRR by optimizing ranking algorithms.

    • Data Collection: Log user clicks and compare against relevance labels.

2. Methods for Collecting Relevant Data

To ensure accurate tracking, data should be collected systematically. The following methods can be utilized:

A. Logging and Search Analytics

  • Implement query logging: Capture search terms, session duration, result clicks, and refinements.

  • Track user interactions: Record scroll depth, mouse movements, and time spent on results.

  • Use A/B testing: Compare search ranking models to measure impact on hit rate and efficiency.

B. User Feedback & Relevance Labeling

  • Explicit relevance feedback: Allow users to rate search results (thumbs up/down, Likert scale).

  • Crowdsourced labeling: Use biomedical domain experts to label relevance for gold-standard datasets.

C. Automated Quality Metrics

  • Re-rank using ML-based relevance scoring: Use NLP models to score biomedical relevance.

  • Use embeddings for semantic search: Improve hit rate by matching concepts beyond keyword matching.

3. Suggested Action Plan

Benchmark Current Performance

  • Establish a baseline for search efficiency and hit rate.

  • Use existing logs to determine the current QSR, P@K, and MRR.

Optimize Query Understanding

  • Implement query expansion (e.g., synonym matching for biomedical terms).

  • Use intent classification to guide ranking models.

Refine Ranking Algorithms

  • Fine-tune weights of search ranking features.

  • Introduce relevance tuning with user feedback loops.

Improve UX for Faster Search

  • Reduce time-to-first-result with prefetching strategies.

  • Implement auto-suggestions to guide users effectively.

Evaluate and Iterate

  • Perform quarterly reviews of search analytics.

  • Introduce controlled experiments (A/B tests) to validate ranking changes.

4. Summary of Key Takeaways

  • Define search efficiency and hit rate metrics (TFRR, QSR, P@K, MRR).

  • Collect data using query logs, feedback mechanisms, and automated relevance labeling.

  • Optimize query understanding, ranking models, and UX design to improve efficiency.

  • Continuously measure and iterate through controlled experiments.