| Autor: |
HAOLUN WU1 haolun.wu@mail.mcgill.ca, CHEN MA2 chenma@cityu.edu.hk, MITRA, BHASKAR3 bhaskar.mitra@microsoft.com, DIAZ, FERNANDO4 diazf@acm.org, XUE LIU4 xueliu@cs.mcgill.ca |
| Předmět: |
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| Zdroj: |
ACM Transactions on Information Systems. Apr2023, Vol. 41 Issue 2, p1-29. 29p. |
| Abstrakt: |
Nowadays, most online services are hosted onmulti-stakeholdermarketplaces, where consumers and producers may have different objectives. Conventional recommendation systems, however, mainly focus on maximizing consumers' satisfaction by recommending the most relevant items to each individual. This may result in unfair exposure of items, thus jeopardizing producer benefits. Additionally, they do not care whether consumers from diverse demographic groups are equally satisfied. To address these limitations, we propose a multi-objective optimization framework for fairness-aware recommendation, Multi-FR, that adaptively balances accuracy and fairness for various stakeholders with Pareto optimality guarantee. We first propose four fairness constraints on consumers and producers. In order to train the whole framework in an end-to-end way, we utilize the smooth rank and stochastic ranking policy to make these fairness criteria differentiable and friendly to back-propagation. Then, we adopt themultiple gradient descent algorithm to generate a Pareto set of solutions, from which the most appropriate one is selected by the Least Misery Strategy. The experimental results demonstrate that Multi-FR largely improves recommendation fairness on multiple stakeholders over the state-of-the-art approaches while maintaining almost the same recommendation accuracy. The training efficiency study confirms our model's ability to simultaneously optimize different fairness constraints for many stakeholders efficiently. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Library, Information Science & Technology Abstracts |
| Externí odkaz: |
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