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Fair Exposure of Documents in Information Retrieval: a Community Detection Approach

Abstract : While (mainly) designed to answer users' needs, search engines and recommendation systems do not necessarily guarantee the exposure of the data they store and index while it can be essential for information providers. A recent research direction so called "fair" exposure of documents tackles this problem in information retrieval. It has mainly been cast into a re-ranking problem with constraints and optimization functions. This paper presents the first steps toward a new framework for fair document exposure. This framework is based on document linking and document community detection; communities are used to rank the documents to be retrieved according to an information need. In addition to the first step of this new framework, we present its potential through both a toy example and a few illustrative examples from the 2019 TREC Fair Ranking Track data set.
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Contributor : Josiane Mothe <>
Submitted on : Monday, June 22, 2020 - 3:48:31 PM
Last modification on : Saturday, October 3, 2020 - 3:29:22 AM


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  • HAL Id : hal-02877632, version 1


Adrian-Gabriel Chifu, Josiane Mothe, Zia Ullah. Fair Exposure of Documents in Information Retrieval: a Community Detection Approach. Circle 2020, Josiane Mothe; Iván Cantador; Max Chevalier; Massimo Melucci, Jul 2020, Samatan, France. ⟨hal-02877632⟩



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