@inproceedings{520cbc948d314a8987c60e540a7d10f9,
title = "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{\textquoteright}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.",
keywords = "bias, recommender systems, search engine",
author = "Harshit Mishra and Sucheta Soundarajan",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023 ; Conference date: 18-09-2023 Through 22-09-2023",
year = "2023",
doi = "10.1007/978-3-031-43421-1_25",
language = "English (US)",
isbn = "9783031434204",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "420--435",
editor = "Danai Koutra and Claudia Plant and {Gomez Rodriguez}, Manuel and Elena Baralis and Francesco Bonchi",
booktitle = "Machine Learning and Knowledge Discovery in Databases",
address = "Germany",
}