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Multistakeholder recommendation with provider constraints

Published: 27 September 2018 Publication History

Abstract

Recommender systems are typically designed to optimize the utility of the end user. In many settings, however, the end user is not the only stakeholder and this exclusive focus may produce unsatisfactory results for other stakeholders. One such setting is found in multisided platforms, which bring together buyers and sellers. In such platforms, it may be necessary to jointly optimize the value for both buyers and sellers. This paper proposes a constraint-based integer programming optimization model, in which different sets of constraints are used to reflect the goals of the different stakeholders. This model is applied as a post-processing step, so it can easily be added onto an existing recommendation system to make it multi-stakeholder aware. For computational tractability with larger data sets, we reformulate the integer problem using the Lagrangian dual and use subgradient optimization. In experiments with two data sets, we evaluate empirically the interaction between the utilities of buyers and sellers and show that our approximation can achieve good upper and lower bounds in practical situations.

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References

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cover image ACM Conferences
RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems
September 2018
600 pages
ISBN:9781450359016
DOI:10.1145/3240323
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 27 September 2018

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Author Tags

  1. constraint-based recommendation
  2. multisided platforms
  3. multistakeholder recommendation

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  • Research-article

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RecSys '18
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RecSys '18: Twelfth ACM Conference on Recommender Systems
October 2, 2018
British Columbia, Vancouver, Canada

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RecSys '18 Paper Acceptance Rate 32 of 181 submissions, 18%;
Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

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  • (2024)Positive-Sum Impact of Multistakeholder Recommender Systems for Urban Tourism Promotion and User UtilityProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688173(939-944)Online publication date: 8-Oct-2024
  • (2024)Towards Sustainable Recommendations in Urban TourismProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688016(1330-1334)Online publication date: 8-Oct-2024
  • (2024)Guaranteeing Accuracy and Fairness under Fluctuating User Traffic: A Bankruptcy-Inspired Re-ranking ApproachProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679590(2991-3001)Online publication date: 21-Oct-2024
  • (2024)Intersectional Two-sided Fairness in RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645518(3609-3620)Online publication date: 13-May-2024
  • (2024)Multi-task Recommendation in Marketplace via Knowledge Attentive Graph Convolutional Network with Adaptive Contrastive Learning2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825433(2162-2169)Online publication date: 15-Dec-2024
  • (2024)Multi-stakeholder recommendations system with deep learning-based diversity personalization and multi-objective optimization for establishing trade-off among competing preferencesKybernetes10.1108/K-02-2024-0344Online publication date: 11-Jul-2024
  • (2023)Algorithmic Governance of Two-Sided Platforms: The Case of Short Video RecommendationSSRN Electronic Journal10.2139/ssrn.4572513Online publication date: 2023
  • (2023)Analysis and UX Improvement of Design Unfairness in Platform Labor Service according to Power Asymmetry among StakeholdersArchives of Design Research10.15187/adr.2023.02.36.1.26536:1(265-277)Online publication date: 28-Feb-2023
  • (2023)RecRec: Algorithmic Recourse for Recommender SystemsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615181(4325-4329)Online publication date: 21-Oct-2023
  • (2023)A Survey on the Fairness of Recommender SystemsACM Transactions on Information Systems10.1145/354733341:3(1-43)Online publication date: 7-Feb-2023
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