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Towards Ethical Item Ranking: A Paradigm Shift from User-Centric to Item-Centric Approaches

Published: 11 July 2024 Publication History

Abstract

Ranking systems are instrumental in shaping user experiences by determining the relevance and order of presented items. However, current approaches, particularly those revolving around user-centric reputation scoring, raise ethical concerns associated with scoring individuals. To counter such issues, in this paper, we introduce a novel item ranking system approach that strategically transitions its emphasis from scoring users to calculating item rankings relying exclusively on items' ratings information, to achieve the same objective. Experiments on three datasets show that our approach achieves higher effectiveness and efficiency than state-of-the-art baselines. Furthermore, the resulting rankings are more robust to spam and resistant to bribery, contributing to a novel and ethically sound direction for item ranking systems.

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Cited By

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  • (2025)Robust Privacy-Preserving Federated Item Ranking in Online Marketplaces: Exploiting Platform Reputation for Effective AggregationIEEE Transactions on Big Data10.1109/TBDATA.2024.350505511:1(303-309)Online publication date: Feb-2025

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      cover image ACM Conferences
      SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
      July 2024
      3164 pages
      ISBN:9798400704314
      DOI:10.1145/3626772
      This work is licensed under a Creative Commons Attribution International 4.0 License.

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      Publication History

      Published: 11 July 2024

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      Author Tags

      1. bias
      2. bribing
      3. effectiveness
      4. efficiency
      5. non-discrimination
      6. ranking system
      7. robustness

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      • (2025)Robust Privacy-Preserving Federated Item Ranking in Online Marketplaces: Exploiting Platform Reputation for Effective AggregationIEEE Transactions on Big Data10.1109/TBDATA.2024.350505511:1(303-309)Online publication date: Feb-2025

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