BalancedQR: A Framework for Balanced Query Recommendation
Abstract
Online search engines are an extremely popular tool for seeking information. However, the results returned sometimes exhibit undesirable or even wrongful forms of imbalance, such as with respect to gender or race. In this paper, we consider the problem of balanced query recommendation , in which the goal is to suggest queries that are relevant to a user’s search query but exhibit less (or opposing) bias than the original query. We present a multi-objective optimization framework that uses word embeddings to suggest alternate keywords for biased keywords present in a search query. We perform a qualitative analysis on pairs of subReddits from Reddit.com (r/Republican vs. r/democrats) as well as a quantitative analysis on data collected from Twitter. Our results demonstrate the efficacy of the proposed method and illustrate subtle linguistic differences between words used by sources with different political leanings.
Cite
Text
Mishra and Soundarajan. "BalancedQR: A Framework for Balanced Query Recommendation." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2023. doi:10.1007/978-3-031-43421-1_25Markdown
[Mishra and Soundarajan. "BalancedQR: A Framework for Balanced Query Recommendation." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2023.](https://mlanthology.org/ecmlpkdd/2023/mishra2023ecmlpkdd-balancedqr/) doi:10.1007/978-3-031-43421-1_25BibTeX
@inproceedings{mishra2023ecmlpkdd-balancedqr,
title = {{BalancedQR: A Framework for Balanced Query Recommendation}},
author = {Mishra, Harshit and Soundarajan, Sucheta},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2023},
pages = {420-435},
doi = {10.1007/978-3-031-43421-1_25},
url = {https://mlanthology.org/ecmlpkdd/2023/mishra2023ecmlpkdd-balancedqr/}
}