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. As the reach of recommender systems has extended, it has become apparent that the single-minded focus on the user common to academic research has obscured other important aspects of recommendation outcomes. Properties such as fairness, balance, profitability, and reciprocity are not captured by typical metrics for recommender system evaluation. The concept of multistakeholder recommendation has emerged as a unifying framework for describing and understanding recommendation settings where the end user is not the sole focus. This article describes the origins of multistakeholder recommendation, and the landscape of system designs. It provides illustrative examples of current research, as well as outlining open questions and research directions for the field.
https://arxiv.org/abs/1905.01986
Abdollahpouri, Himan, et al. "Beyond Personalization: Research Directions in Multistakeholder Recommendation." arXiv preprint arXiv:1905.01986 (2019).
@misc{abdollahpouri2019personalization,
title={Beyond Personalization: Research Directions in Multistakeholder Recommendation},
author={Himan Abdollahpouri and Gediminas Adomavicius and Robin Burke and Ido Guy and Dietmar Jannach and Toshihiro Kamishima and Jan Krasnodebski and Luiz Pizzato},
year={2019},
eprint={1905.01986},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
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Recommender systems research often requires the creation and execution of large numbers of algorithmic experiments to determine the sensitivity of results to the values of various hyperparameters. Existing recommender systems platforms fail to provide a basis for systematic experimentation of this type. In this paper, we describe librec-auto, a wrapper for the well-known LibRec library, which provides an environment that supports automated experimentation.
Mansoury, Masoud, Robin Burke, Aldo Ordonez-Gauger and Xavier Sepulveda. "Automating Recommender Systems Experimentation with librec-auto." Proceedings of the 12th ACM Conference on Recommender Systems. ACM, 2018.
@inproceedings{mansoury2018librecauto,
title={Automating Recommender Systems Experimentation with librec-auto},
author={Mansoury, Masoud and Burke, Robin and Ordo{\~{n}}ez-Gauger, Aldo and Sepulveda, Xavier},
booktitle={Proceedings of the Twelfth ACM Conference on Recommender Systems},
pages={500--501},
year={2018},
organization={ACM}
}
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When evaluating recommender systems for their fairness to users, it may be necessary to make use of demographic attributes, which are personally sensitive and usually excluded from publicly-available data sets. In addition, these attributes are fixed and therefore it is not possible to experiment with different distributions using the same data. In this paper, we describe the Frequency-Linked Attribute Generation (FLAG) algorithm, and show its applicability for assigning synthetic demographic attributes to recommendation data sets.
Burke, Robin, Jackson Kontny, and Nasim Sonboli. "Synthetic Attribute Data for Evaluating Consumer-side Fairness." FATRec Workshop on Responsible Recommendation. 2018
@inproceedings{burke018synthetic,
title={Synthetic Attribute Data for Evaluating Consumer-side Fairness},
author={Burke, Robin and Kontny, Jackson and Sonboli, Nasim},
booktitle={FATRec Workshop on Responsible Recommendation},
pages={to appear},
year={2018}
}
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Personalized recommendation brings about novel challenges in ensuring fairness, especially in scenarios in which users are not the only stakeholders involved in the recommender system. For example, the system may want to ensure that items from different providers have a fair chance of being recommended. To solve this problem, we propose a Fairness-Aware Re-ranking algorithm (FAR) to balance the ranking quality and multi-sided fairness. We iteratively generate the ranking list by trading off between accuracy and the coverage for the providers. Although fair treatment of providers is desirable, users may differ in their receptivity to the addition of this type of diversity. Therefore, personalized user tolerance towards provider diversification is incorporated. Experiments are conducted on both synthetic and real-world data. The results show that our proposed re-ranking algorithm can significantly promote fairness with a slight sacrifice in accuracy and can do so while being attentive to individual user differences.
Liu, Weiwen, and Robin Burke. "Personalizing Fairness-aware Re-ranking." FATRec Workshop on Responsible Recommendation. 2018
@inproceedings{liu2018personalizing,
title={Personalizing Fairness-aware Re-ranking},
author={Liu, Weiwen and Burke, Robin},
booktitle={FATRec Workshop on Responsible Recommendation},
pages={to appear},
year={2018}
}
]]>