extended-abstract

Comparing recommender systems using synthetic data

Author:

Manel SlokomAuthors Info & Claims

RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems

Pages 548 - 552

https://doi.org/10.1145/3240323.3240325

Published: 27 September 2018 Publication History

Abstract

In this work, we propose SynRec, a data protection framework that uses data synthesis. The goal is to protect sensitive information in the user-item matrix by replacing the original values with synthetic values or, alternatively, completely synthesizing new users. The synthetic data must fulfill two requirements. First, it must no longer be possible to derive certain sensitive information from the data, and, second, it must remain possible to use the synthetic data for comparing recommender systems. SynRec is a step towards making it possible for companies to release recommender system data to the research community for the development of new algorithms, for example, in the context of recommender system challenges. We report the results of preliminary experiments, which provide a proof-of-concept, and also describe the future research directions, i.e., the challenges that must be addressed in order to make the framework useful in practice.

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Cited By

Skittou MMerrouchi MGadi T(2024)A Recommender System for Educational PlanningCybernetics and Information Technologies10.2478/cait-2024-001624:2(67-85)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.2478/cait-2024-0016
Beel JWegmeth LMichiels LSchulz S(2024)Informed Dataset Selection with ‘Algorithm Performance Spaces’Proceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691704(1085-1090)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691704
Di Fazio A(2024)Enhancing Privacy in Recommender Systems through Differential Privacy TechniquesProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688019(1348-1352)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688019
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Index Terms

Comparing recommender systems using synthetic data
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Information & Contributors

Information

Published In

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

General Chairs:
Sole Pera
Boise State University
,
Michael Ekstrand
Boise State University
,
Program Chairs:
Xavier Amatriain
Curai, USA
,
John O'Donovan
University of California, Santa Barbara

Copyright © 2018 Owner/Author.

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 September 2018

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RecSys '18

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SIGCHI

RecSys '18: Twelfth ACM Conference on Recommender Systems

October 2, 2018

British Columbia, Vancouver, Canada

Acceptance Rates

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

Skittou MMerrouchi MGadi T(2024)A Recommender System for Educational PlanningCybernetics and Information Technologies10.2478/cait-2024-001624:2(67-85)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.2478/cait-2024-0016
Beel JWegmeth LMichiels LSchulz S(2024)Informed Dataset Selection with ‘Algorithm Performance Spaces’Proceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691704(1085-1090)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691704
Di Fazio A(2024)Enhancing Privacy in Recommender Systems through Differential Privacy TechniquesProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688019(1348-1352)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688019
Zhang GLi DGu HLu TShang LGu N(2024)Simulating News Recommendation Ecosystems for Insights and ImplicationsIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.338132911:5(5699-5713)Online publication date: Oct-2024
https://doi.org/10.1109/TCSS.2024.3381329
Ferreira LSilva DUriarte-Itzazelaia M(2024)SNOOKER: a dataset generator for helpdesk servicesJournal of Intelligent Information Systems10.1007/s10844-024-00905-5Online publication date: 8-Nov-2024
https://doi.org/10.1007/s10844-024-00905-5
del Carmen Rodríguez-Hernández MIlarri SCaballero MTrillo-Lado RHermoso Rdel-Hoyo-Alonso R(2024)AUTO-DataGenCARS+: An Advanced User-Oriented Tool to Generate Data for the Evaluation of Recommender SystemsAdvances in Mobile Computing and Multimedia Intelligence10.1007/978-3-031-78049-3_16(176-191)Online publication date: 28-Nov-2024
https://doi.org/10.1007/978-3-031-78049-3_16
Iantorno MGarza J(2024)Leveraging Artificial Intelligence Models Using Synthetic DataProceedings of the Future Technologies Conference (FTC) 2024, Volume 110.1007/978-3-031-73110-5_4(56-66)Online publication date: 5-Nov-2024
https://doi.org/10.1007/978-3-031-73110-5_4
Li XChen KZhou YChen Y(2023)A Generative Adversarial Approach with Social Relationship for Recommender SystemsProceedings of the 2023 International Conference on Power, Communication, Computing and Networking Technologies10.1145/3630138.3630424(1-5)Online publication date: 24-Sep-2023
https://dl.acm.org/doi/10.1145/3630138.3630424
Stavinova EGurov ALysenko AChunaev P(2023)Performance Ranking of Recommender Systems on Simulated DataProcedia Computer Science10.1016/j.procs.2022.10.216212:C(142-151)Online publication date: 20-Jan-2023
https://dl.acm.org/doi/10.1016/j.procs.2022.10.216
Balog KMaxwell DThomas PZhang S(2022)Report on the 1st simulation for information retrieval workshop (Sim4IR 2021) at SIGIR 2021ACM SIGIR Forum10.1145/3527546.352755955:2(1-16)Online publication date: 17-Mar-2022
https://dl.acm.org/doi/10.1145/3527546.3527559
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