# Offline Retrieval Evaluation Without Evaluation Metrics

arXiv: https://arxiv.org/abs/2204.11400 · HTML: https://mlsanity.com/paper/2204.11400/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2022.

## Built on by (most cited first, 2 of 2)

- [Offline Preference-Based Trajectory Evaluation](https://mlsanity.com/paper/2606.17541/) (2026)
- [Advanced Acceptance Score: A Holistic Measure for Biometric Quantification](https://mlsanity.com/paper/2602.15535/) (2026)

## Abstract (arXiv)

Offline evaluation of information retrieval and recommendation has traditionally focused on distilling the quality of a ranking into a scalar metric such as average precision or normalized discounted cumulative gain. We can use this metric to compare the performance of multiple systems for the same request. Although evaluation metrics provide a convenient summary of system performance, they also collapse subtle differences across users into a single number and can carry assumptions about user behavior and utility not supported across retrieval scenarios. We propose recall-paired preference (RPP), a metric-free evaluation method based on directly computing a preference between ranked lists. RPP simulates multiple user subpopulations per query and compares systems across these pseudo-populations. Our results across multiple search and recommendation tasks demonstrate that RPP substantially improves discriminative power while correlating well with existing metrics and being equally robust to incomplete data.