# Fast Interactive Search with a Scale-Free Comparison Oracle

arXiv: https://arxiv.org/abs/2306.01814 · HTML: https://mlsanity.com/paper/2306.01814/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 2 Jun 2023.

## Builds on (most cited first, 8 of 8)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Multidimensional Binary Search for Contextual Decision-Making](https://mlsanity.com/paper/1611.00829/) (2016)
- [Active classification with comparison queries](https://mlsanity.com/paper/1704.03564/) (2017)
- [Cost-Effective HITs for Relative Similarity Comparisons](https://mlsanity.com/paper/1404.3291/) (2014)
- [Active embedding search via noisy paired comparisons](https://mlsanity.com/paper/1905.04363/) (2019)
- [Insights into Ordinal Embedding Algorithms: A Systematic Evaluation](https://mlsanity.com/paper/1912.01666/) (2019)
- [Comparison Based Nearest Neighbor Search](https://mlsanity.com/paper/1704.01460/) (2017)
- [Comparison Based Learning from Weak Oracles](https://mlsanity.com/paper/1802.06942/) (2018)

## Abstract (arXiv)

A comparison-based search algorithm lets a user find a target item t in a database by answering queries of the form, ``Which of items i and j is closer to t?'' Instead of formulating an explicit query (such as one or several keywords), the user navigates towards the target via a sequence of such (typically noisy) queries. We propose a scale-free probabilistic oracle model called γ-CKL for such similarity triplets (i,j;t), which generalizes the CKL triplet model proposed in the literature. The generalization affords independent control over the discriminating power of the oracle and the dimension of the feature space containing the items. We develop a search algorithm with provably exponential rate of convergence under the γ-CKL oracle, thanks to a backtracking strategy that deals with the unavoidable errors in updating the belief region around the target. We evaluate the performance of the algorithm both over the posited oracle and over several real-world triplet datasets. We also report on a comprehensive user study, where human subjects navigate a database of face portraits.