Fair Sequential Recommendation without User Demographics

Huimin Zeng, Zhankui He, Zhenrui Yue, Julian McAuley, Dong Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Much existing literature on fair recommendation (i.e., group fairness) leverages users' demographic attributes (e.g., gender) to develop fair recommendation methods. However, in real-world scenarios, due to privacy concerns and convenience considerations, users may not be willing to share their demographic information with the system, which limits the application of many existing methods. Moreover, sequential recommendation (SR) models achieve state-of-the-art performance compared to traditional collaborative filtering (CF) recommenders, and can represent users solely using user-item interactions (user-free). This leaves a wrong impression that SR models are free from group unfairness by design. In this work, we explore a critical question: how can we build a fair sequential recommendation system without even knowing user demographics? To address this problem, we propose Agnostic FairSeqRec (A-FSR): a model-agnostic and demographic-agnostic debiasing framework for sequential recommendation without requiring users' demographic attributes. Firstly, A-FSR reduces the correlation between the potential stereotypical patterns in the input sequences and final recommendations via Dirichlet neighbor smoothing. Secondly, A-FSR estimates an under-represented group of sequences via a gradient-based heuristic, and implicitly moves training focus towards the under-represented group by minimizing a distributionally robust optimization (DRO) based objective. Results on real-world datasets show that A-FSR achieves significant improvements on group fairness in sequential recommendation, while outperforming other state-of-the-art baselines.

Original languageEnglish (US)
Title of host publicationSIGIR 2024 - Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery
Pages395-404
Number of pages10
ISBN (Electronic)9798400704314
DOIs
StatePublished - Jul 10 2024
Event47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024 - Washington, United States
Duration: Jul 14 2024Jul 18 2024

Publication series

NameSIGIR 2024 - Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Conference47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024
Country/TerritoryUnited States
CityWashington
Period7/14/247/18/24

Keywords

  • demographic agnostic
  • group fairness
  • model agnostic
  • recommender systems
  • sequential recommendation

ASJC Scopus subject areas

  • Information Systems
  • Software

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