BalancedQR: A Framework for Balanced Query Recommendation

Harshit Mishra, Sucheta Soundarajan

Research output: Chapter in Book/Entry/PoemConference contribution


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 (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.

Original languageEnglish (US)
Title of host publicationMachine Learning and Knowledge Discovery in Databases
Subtitle of host publicationResearch Track - European Conference, ECML PKDD 2023, Proceedings
EditorsDanai Koutra, Claudia Plant, Manuel Gomez Rodriguez, Elena Baralis, Francesco Bonchi
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages16
ISBN (Print)9783031434204
StatePublished - 2023
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023 - Turin, Italy
Duration: Sep 18 2023Sep 22 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14172 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023


  • bias
  • recommender systems
  • search engine

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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