# Metrics for measuring the tradeoff between efficiency and effectiveness in Information Retrieval

I'm comparing several methods for building inverted indexes (n-grams, hash boundaries, etc.). Given a set of queries, these indexes perform differently. On one hand, I get performances metrics (efficiency) such as the time needed to execute the query. On the other hand, I get metrics such as precision and recall (effectiveness) by checking the relevance of the results. Depending on the method I use, there is a tradeoff between efficiency and effectiveness. For example, some methods loose a little in term of effectiveness for a huge gain in term of efficiency.

Are there some metrics which puts the effectiveness (precision, recall, etc.) in perspective with the efficiency (throughput, memory, etc.)?

• What do you think? What candidates have you found, what discussion? – vonbrand Jan 27 '16 at 12:00
• Thank you for your comment, I tried to clarify the question. – Bertil Chapuis Jan 27 '16 at 13:23

This is mainly because these two issues can sometimes be quite independent between each other. We use certain methods to build inverted indexes in order to mitigate memory issues that arise when we try to index large collections of documents. Some of these methods (such as hash tables) provide fast access to the dictionary (in $O(1)$ time) but fail to accomodate proximity queries (the sort of "find documents that have the word "Beatles" near the word "Animals"). Moreover there are implications arising if we are to index a collection that might change in size and content. Usage of B-trees can overcome these issues as these structures permit an ordering of their contents.