Search Results for author: Nasim Sonboli

Found 8 papers, 1 papers with code

Towards Confidence-aware Calibrated Recommendation

no code implementations22 Aug 2022 Mohammadmehdi Naghiaei, Hossein A. Rahmani, Mohammad Aliannejadi, Nasim Sonboli

Calibration ensures that the distribution of recommended item categories is consistent with the user's historical data.

Recommendation Systems Re-Ranking

Fairness and Transparency in Recommendation: The Users' Perspective

no code implementations16 Mar 2021 Nasim Sonboli, Jessie J. Smith, Florencia Cabral Berenfus, Robin Burke, Casey Fiesler

Even though the previous work in other branches of AI has explored the use of explanations as a tool to increase fairness, this work has not been focused on recommendation.

Fairness Recommendation Systems

"And the Winner Is...": Dynamic Lotteries for Multi-group Fairness-Aware Recommendation

no code implementations5 Sep 2020 Nasim Sonboli, Robin Burke, Nicholas Mattei, Farzad Eskandanian, Tian Gao

As recommender systems are being designed and deployed for an increasing number of socially-consequential applications, it has become important to consider what properties of fairness these systems exhibit.

Fairness Recommendation Systems

Opportunistic Multi-aspect Fairness through Personalized Re-ranking

no code implementations21 May 2020 Nasim Sonboli, Farzad Eskandanian, Robin Burke, Weiwen Liu, Bamshad Mobasher

In this paper, we present a re-ranking approach to fairness-aware recommendation that learns individual preferences across multiple fairness dimensions and uses them to enhance provider fairness in recommendation results.

Attribute Fairness +2

Exploring User Opinions of Fairness in Recommender Systems

no code implementations13 Mar 2020 Jessie Smith, Nasim Sonboli, Casey Fiesler, Robin Burke

Algorithmic fairness for artificial intelligence has become increasingly relevant as these systems become more pervasive in society.

Fairness Recommendation Systems

Crank up the volume: preference bias amplification in collaborative recommendation

no code implementations13 Sep 2019 Kun Lin, Nasim Sonboli, Bamshad Mobasher, Robin Burke

Recommender systems are personalized: we expect the results given to a particular user to reflect that user's preferences.

Recommendation Systems

Power of the Few: Analyzing the Impact of Influential Users in Collaborative Recommender Systems

no code implementations14 May 2019 Farzad Eskandanian, Nasim Sonboli, Bamshad Mobasher

Like other social systems, in collaborative filtering a small number of "influential" users may have a large impact on the recommendations of other users, thus affecting the overall behavior of the system.

Collaborative Filtering Recommendation Systems

Synthetic Attribute Data for Evaluating Consumer-side Fairness

1 code implementation12 Sep 2018 Robin Burke, Jackson Kontny, Nasim Sonboli

When evaluating recommender systems for their fairness, it may be necessary to make use of demographic attributes, which are personally sensitive and usually excluded from publicly-available data sets.

Computers and Society

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