TFROM: A two-sided fairness-aware recommendation model for both customers and providers

Yao WU, Jian CAO, Guandong XU, Yudong TAN

Research output: Chapter in Book/Report/Conference proceedingChapters

35 Citations (Scopus)

Abstract

At present, most research on the fairness of recommender systems is conducted either from the perspective of customers or from the perspective of product (or service) providers. However, such a practice ignores the fact that when fairness is guaranteed to one side, the fairness and rights of the other side are likely to reduce. In this paper, we consider recommendation scenarios from the perspective of two sides (customers and providers). From the perspective of providers, we consider the fairness of the providers' exposure in recommender system. For customers, we consider the fairness of the reduced quality of recommendation results due to the introduction of fairness measures. We theoretically analyzed the relationship between recommendation quality, customers fairness, and provider fairness, and design a two-sided fairness-aware recommendation model (TFROM) for both customers and providers. Specifically, we design two versions of TFROM for offline and online recommendation. The effectiveness of the model is verified on three real-world data sets. The experimental results show that TFROM provides better two-sided fairness while still maintaining a higher level of personalization than the baseline algorithms. Copyright © 2021 Association for Computing Machinery.

Original languageEnglish
Title of host publicationProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
Place of PublicationNew York
PublisherThe Association for Computing Machinery
Pages1013-1022
ISBN (Electronic)9781450380379
DOIs
Publication statusPublished - Jul 2021

Citation

Wu, Y., Cao, J., Xu, G., & Tan, Y. (2021). TFROM: A two-sided fairness-aware recommendation model for both customers and providers. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1013-1022). The Association for Computing Machinery. https://doi.org/10.1145/3404835.3462882

Keywords

  • Two-sided fairness
  • Fairness-aware recommendation
  • Customer fairness
  • Provider fairness

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