@inproceedings{afa2fae5375e44819425ad623a4dff1d,
title = "Facets of fairness in search and recommendation",
abstract = "Several recent works have highlighted how search and recommender systems exhibit bias along different dimensions. Counteracting this bias and bringing a certain amount of fairness in search is crucial to not only creating a more balanced environment that considers relevance and diversity but also providing a more sustainable way forward for both content consumers and content producers. This short paper examines some of the recent works to define relevance, diversity, and related concepts. Then, it focuses on explaining the emerging concept of fairness in various recommendation settings. In doing so, this paper presents comparisons and highlights contracts among various measures, and gaps in our conceptual and evaluative frameworks.",
keywords = "Evaluation metrics, Fair ranking, Fairness, Fairness in recommendation, Search bias",
author = "Sahil Verma and Ruoyuan Gao and Chirag Shah",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 1st International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2020, held as part of the 42nd European Conference on Information Retrieval, ECIR 2020 ; Conference date: 14-04-2020 Through 14-04-2020",
year = "2020",
doi = "10.1007/978-3-030-52485-2_1",
language = "English (US)",
isbn = "9783030524845",
series = "Communications in Computer and Information Science",
publisher = "Springer",
pages = "1--11",
editor = "Ludovico Boratto and Stefano Faralli and Mirko Marras and Giovanni Stilo",
booktitle = "Bias and Social Aspects in Search and Recommendation - 1st International Workshop, BIAS 2020, Proceedings",
}