# Exposing Query Identification for Search Transparency

arXiv: https://arxiv.org/abs/2110.07701 · HTML: https://mlsanity.com/paper/2110.07701/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 54 references that are in the map.
- First posted on arXiv 14 Oct 2021.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://mlsanity.com/paper/2007.00808/) (2020)
- [Document Expansion by Query Prediction](https://mlsanity.com/paper/1904.08375/) (2019)
- [Overview of the TREC 2020 deep learning track](https://mlsanity.com/paper/2102.07662/) (2021)
- [Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search](https://mlsanity.com/paper/1905.01989/) (2019)
- [Evaluating Stochastic Rankings with Expected Exposure](https://mlsanity.com/paper/2004.13157/) (2020)
- [EXS: Explainable Search Using Local Model Agnostic Interpretability](https://mlsanity.com/paper/1809.03857/) (2018)
- [Posthoc Interpretability of Learning to Rank Models using Secondary Training Data](https://mlsanity.com/paper/1806.11330/) (2018)
- [Interpreting Neural Ranking Models using Grad-CAM](https://mlsanity.com/paper/2005.05768/) (2020)
- [Active Metric Learning for Supervised Classification](https://mlsanity.com/paper/1803.10647/) (2018)
- [Interpreting search result rankings through intent modeling](https://mlsanity.com/paper/1809.05190/) (2018)
- [Interpretable Learning-to-Rank with Generalized Additive Models](https://mlsanity.com/paper/2005.02553/) (2020)
- [Helping results assessment by adding explainable elements to the deep relevance matching model](https://mlsanity.com/paper/2106.05147/) (2021)
- [Studying Ranking-Incentivized Web Dynamics](https://mlsanity.com/paper/2005.13810/) (2020)

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

- [Improving Content Retrievability in Search with Controllable Query Generation](https://mlsanity.com/paper/2303.11648/) (2023)
- [Ethical and Social Considerations in Automatic Expert Identification and People Recommendation in Organizational Knowledge Management Systems](https://mlsanity.com/paper/2209.03819/) (2022)

## Abstract (arXiv)

Search systems control the exposure of ranked content to searchers. In many cases, creators value not only the exposure of their content but, moreover, an understanding of the specific searches where the content is surfaced. The problem of identifying which queries expose a given piece of content in the ranking results is an important and relatively under-explored search transparency challenge. Exposing queries are useful for quantifying various issues of search bias, privacy, data protection, security, and search engine optimization. Exact identification of exposing queries in a given system is computationally expensive, especially in dynamic contexts such as web search. We explore the feasibility of approximate exposing query identification (EQI) as a retrieval task by reversing the role of queries and documents in two classes of search systems: dense dual-encoder models and traditional BM25 models. We then propose how this approach can be improved through metric learning over the retrieval embedding space. We further derive an evaluation metric to measure the quality of a ranking of exposing queries, as well as conducting an empirical analysis focusing on various practical aspects of approximate EQI. Overall, our work contributes a novel conception of transparency in search systems and computational means of achieving it.