Search Results for author: Robin Burke

Found 30 papers, 5 papers with code

Exploring Social Choice Mechanisms for Recommendation Fairness in SCRUF

1 code implementation10 Sep 2023 Amanda Aird, Cassidy All, Paresha Farastu, Elena Stefancova, Joshua Sun, Nicholas Mattei, Robin Burke

Fairness problems in recommender systems often have a complexity in practice that is not adequately captured in simplified research formulations.

Fairness Recommendation Systems

Dynamic fairness-aware recommendation through multi-agent social choice

no code implementations2 Mar 2023 Amanda Aird, Paresha Farastu, Joshua Sun, Amy Voida, Nicholas Mattei, Robin Burke

Algorithmic fairness in the context of personalized recommendation presents significantly different challenges to those commonly encountered in classification tasks.

Fairness Recommendation Systems

Who Pays? Personalization, Bossiness and the Cost of Fairness

no code implementations8 Sep 2022 Paresha Farastu, Nicholas Mattei, Robin Burke

The concern is that a bossy user may be able to shift the cost of fairness to others, improving their own outcomes and worsening those for others.

Fairness Recommendation Systems

Fairness in Information Access Systems

no code implementations12 May 2021 Michael D. Ekstrand, Anubrata Das, Robin Burke, Fernando Diaz

Recommendation, information retrieval, and other information access systems pose unique challenges for investigating and applying the fairness and non-discrimination concepts that have been developed for studying other machine learning systems.

Fairness Information Retrieval +1

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

User-centered Evaluation of Popularity Bias in Recommender Systems

no code implementations10 Mar 2021 Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher, Edward Malthouse

In this paper, we show the limitations of the existing metrics to evaluate popularity bias mitigation when we want to assess these algorithms from the users' perspective and we propose a new metric that can address these limitations.

Recommendation Systems

User Factor Adaptation for User Embedding via Multitask Learning

1 code implementation EACL (AdaptNLP) 2021 Xiaolei Huang, Michael J. Paul, Robin Burke, Franck Dernoncourt, Mark Dredze

In this study, we treat the user interest as domains and empirically examine how the user language can vary across the user factor in three English social media datasets.

Clustering text-classification +1

"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

The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation

no code implementations21 Aug 2020 Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher

Moreover, we show that the more a group is affected by the algorithmic popularity bias, the more their recommendations are miscalibrated.

Fairness Recommendation Systems

Feedback Loop and Bias Amplification in Recommender Systems

no code implementations25 Jul 2020 Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, Robin Burke

Recommendation algorithms are known to suffer from popularity bias; a few popular items are recommended frequently while the majority of other items are ignored.

Recommendation Systems

Addressing the Multistakeholder Impact of Popularity Bias in Recommendation Through Calibration

no code implementations23 Jul 2020 Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher

The effectiveness of these approaches, however, has not been assessed in multistakeholder environments where in addition to the users who receive the recommendations, the utility of the suppliers of the recommended items should also be considered.

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

FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems

no code implementations3 May 2020 Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, Robin Burke

That leads to low coverage of items in recommendation lists across users (i. e. low aggregate diversity) and unfair distribution of recommended items.

Fairness Recommendation Systems

Unfair Exposure of Artists in Music Recommendation

no code implementations25 Mar 2020 Himan Abdollahpouri, Robin Burke, Masoud Mansoury

It is well-known that the recommendation algorithms suffer from popularity bias; few popular items are over-recommended which leads to the majority of other items not getting proportionate attention.

Fairness Music Recommendation +1

Developing a Recommendation Benchmark for MLPerf Training and Inference

no code implementations16 Mar 2020 Carole-Jean Wu, Robin Burke, Ed H. Chi, Joseph Konstan, Julian McAuley, Yves Raimond, Hao Zhang

Deep learning-based recommendation models are used pervasively and broadly, for example, to recommend movies, products, or other information most relevant to users, in order to enhance the user experience.

Image Classification object-detection +3

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

The Impact of Popularity Bias on Fairness and Calibration in Recommendation

no code implementations13 Oct 2019 Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher

In this paper, we use a metric called miscalibration for measuring how a recommendation algorithm is responsive to users' true preferences and we consider how various algorithms may result in different degrees of miscalibration.

Fairness Recommendation Systems

Research Commentary on Recommendations with Side Information: A Survey and Research Directions

no code implementations19 Sep 2019 Zhu Sun, Qing Guo, Jie Yang, Hui Fang, Guibing Guo, Jie Zhang, Robin Burke

This Research Commentary aims to provide a comprehensive and systematic survey of the recent research on recommender systems with side information.

Knowledge Graphs Recommendation Systems +1

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

The Unfairness of Popularity Bias in Recommendation

3 code implementations31 Jul 2019 Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher

Recommender systems are known to suffer from the popularity bias problem: popular (i. e. frequently rated) items get a lot of exposure while less popular ones are under-represented in the recommendations.

Recommendation Systems

Multi-stakeholder Recommendation and its Connection to Multi-sided Fairness

no code implementations30 Jul 2019 Himan Abdollahpouri, Robin Burke

There is growing research interest in recommendation as a multi-stakeholder problem, one where the interests of multiple parties should be taken into account.

Fairness Recommendation Systems

Flatter is better: Percentile Transformations for Recommender Systems

no code implementations10 Jul 2019 Masoud Mansoury, Robin Burke, Bamshad Mobasher

This transformation flattens the rating distribution, better compensates for differences in rating distributions, and improves recommendation performance.

Recommendation Systems

Reducing Popularity Bias in Recommendation Over Time

no code implementations27 Jun 2019 Himan Abdollahpouri, Robin Burke

Many recommendation algorithms suffer from popularity bias: a small number of popular items being recommended too frequently, while other items get insufficient exposure.

Beyond Personalization: Research Directions in Multistakeholder Recommendation

no code implementations1 May 2019 Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, Luiz Pizzato

Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes.

Fairness Recommendation Systems

Managing Popularity Bias in Recommender Systems with Personalized Re-ranking

no code implementations22 Jan 2019 Himan Abdollahpouri, Robin Burke, Bamshad Mobasher

Many recommender systems suffer from popularity bias: popular items are recommended frequently while less popular, niche products, are recommended rarely or not at all.

Recommendation Systems Re-Ranking

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

Popularity-Aware Item Weighting for Long-Tail Recommendation

no code implementations15 Feb 2018 Himan Abdollahpouri, Robin Burke, Bamshad Mobasher

Many recommender systems suffer from the popularity bias problem: popular items are being recommended frequently while less popular, niche products, are recommended rarely if not at all.

Recommendation Systems

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