These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.
Precision@k
Precision@k is an evaluation metric used in information retrieval to measure the proportion of relevant items within the top k results returned by a system. It focuses on the accuracy of the top-ranked items, making it particularly useful in scenarios where users are most likely to consider only the initial results.
Formula:
Precision@k = true positives@k / (true positives@k + false positives@k)
Where:
• true positives@k is the number of relevant items within the top k results.
• false positives@k is the number of irrelevant items within the top k results.
Example Usage:
Consider a search engine that returns a list of documents in response to a query. If the top 5 results (k = 5) contain 3 relevant documents, then:
Precision@5 = 3 / 5 = 0.6
This indicates that 60% of the top 5 results are relevant to the user’s query.
Applications and Impact:
• Information Retrieval: Precision@k helps evaluate the effectiveness of search engines by assessing the relevance of the top results presented to users.
• Recommendation Systems: It aids in determining how well a system recommends pertinent items within the top k suggestions, enhancing user satisfaction.
• Performance Benchmarking: Precision@k serves as a benchmark to compare different retrieval or recommendation algorithms, guiding improvements in system design.
Advantages:
• Simplicity: The metric is straightforward to compute and interpret.
• Focus on Top Results: It emphasizes the quality of the most visible results, aligning with typical user behavior.
Limitations:
• Position Ignorance: Precision@k does not account for the order of relevant items within the top k results. For instance, having relevant items at the top is more desirable than at the bottom, but Precision@k treats both scenarios equally.
• Fixed Cut-off: The choice of k can be arbitrary and may not reflect all user interactions, especially if users tend to look beyond the top k results.
References
Järvelin, K. (2017). “IR evaluation methods for retrieving highly relevant documents.” ACM SIGIR Forum, 51(2), 243–250.
About the metric
You can click on the links to see the associated metrics
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