Search Results for author: Nava Tintarev

Found 11 papers, 1 papers with code

This Is Not What We Ordered: Exploring Why Biased Search Result Rankings Affect User Attitudes on Debated Topics

no code implementations4 May 2021 Tim Draws, Nava Tintarev, Ujwal Gadiraju, Alessandro Bozzon, Benjamin Timmermans

To better understand the mechanisms underlying SEME, we present a pre-registered, 5 × 3 factorial user study investigating whether order effects (i. e., users adopting the viewpoint pertaining to higher-ranked documents) can cause SEME.

Disparate Impact Diminishes Consumer Trust Even for Advantaged Users

no code implementations29 Jan 2021 Tim Draws, Zoltán Szlávik, Benjamin Timmermans, Nava Tintarev, Kush R. Varshney, Michael Hind

Systems aiming to aid consumers in their decision-making (e. g., by implementing persuasive techniques) are more likely to be effective when consumers trust them.

Decision Making Fairness Human-Computer Interaction

Operationalizing Framing to Support Multiperspective Recommendations of Opinion Pieces

no code implementations15 Jan 2021 Mats Mulder, Oana Inel, Jasper Oosterman, Nava Tintarev

We apply this notion to a re-ranking of topic-relevant recommended lists, to form the basis of a novel viewpoint diversification method.

Recommendation Systems Re-Ranking

Assessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics

no code implementations27 Oct 2020 Tim Draws, Nava Tintarev, Ujwal Gadiraju, Alessandro Bozzon, Benjamin Timmermans

The way pages are ranked in search results influences whether the users of search engines are exposed to more homogeneous, or rather to more diverse viewpoints.

Fairness

Helping users discover perspectives: Enhancing opinion mining with joint topic models

no code implementations23 Oct 2020 Tim Draws, Jody Liu, Nava Tintarev

Support or opposition concerning a debated claim such as abortion should be legal can have different underlying reasons, which we call perspectives.

Opinion Mining Topic Models

Contextual Personalized Re-Ranking of Music Recommendations through Audio Features

no code implementations6 Sep 2020 Boning Gong, Mesut Kaya, Nava Tintarev

We compare a global (context for all users) and personalized (context for each user) model based on these audio features.

Re-Ranking

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