# HodgeRank with Information Maximization for Crowdsourced Pairwise Ranking Aggregation

arXiv: https://arxiv.org/abs/1711.05957 · HTML: https://mlsanity.com/paper/1711.05957/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2017.

## Built on by (most cited first, 4 of 4)

- [Towards Global Optimal Visual In-Context Learning Prompt Selection](https://mlsanity.com/paper/2405.15279/) (2024)
- [Ranking with Confidence for Large Scale Comparison Data](https://mlsanity.com/paper/2202.01670/) (2022)
- [Pairwise Comparisons Are All You Need](https://mlsanity.com/paper/2403.09746/) (2024)
- [Improving Maximum Likelihood Difference Scaling method to measure inter content scale](https://mlsanity.com/paper/2203.13186/) (2022)

## Abstract (arXiv)

Recently, crowdsourcing has emerged as an effective paradigm for human-powered large scale problem solving in various domains. However, task requester usually has a limited amount of budget, thus it is desirable to have a policy to wisely allocate the budget to achieve better quality. In this paper, we study the principle of information maximization for active sampling strategies in the framework of HodgeRank, an approach based on Hodge Decomposition of pairwise ranking data with multiple workers. The principle exhibits two scenarios of active sampling: Fisher information maximization that leads to unsupervised sampling based on a sequential maximization of graph algebraic connectivity without considering labels; and Bayesian information maximization that selects samples with the largest information gain from prior to posterior, which gives a supervised sampling involving the labels collected. Experiments show that the proposed methods boost the sampling efficiency as compared to traditional sampling schemes and are thus valuable to practical crowdsourcing experiments.