short-paper

Simulating the Impact of Recommender Systems on the Evolution of Collective Users' Choices

Authors:

Naieme Hazrati,

Francesco RicciAuthors Info & Claims

HT '20: Proceedings of the 31st ACM Conference on Hypertext and Social Media

Pages 207 - 212

https://doi.org/10.1145/3372923.3404812

Published: 13 July 2020 Publication History

Abstract

The major focus of recommender systems (RSs) research is on improving the goodness of the generated recommendations. Less attention has been dedicated to understand the effect of an RS on the actual users' choices. Hence, in this paper, we propose a novel simulation model of users' choices under the influence of an RS. The model leverages real rating/choice data observed up to a point in time in order to simulate next, month-by-month, choices of the users. We have analysed choice diversity, popularity and utility and found that: RSs have different effects on the users' choices; the behaviour of new users is particularly important to understand collective choices; and the users' previous knowledge, i.e., their "awareness" of the item catalogue greatly affects choice diversity.

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Cited By

Rahdari BBrusilovsky PKveton B(2024)Towards Simulation-Based Evaluation of Recommender Systems with Carousel InterfacesACM Transactions on Recommender Systems10.1145/36437092:1(1-25)Online publication date: 30-Jan-2024
https://dl.acm.org/doi/10.1145/3643709
Porcaro LGómez ECastillo C(2023)Assessing the Impact of Music Recommendation Diversity on Listeners: A Longitudinal StudyACM Transactions on Recommender Systems10.1145/36084872:1(1-47)Online publication date: 12-Jul-2023
https://dl.acm.org/doi/10.1145/3608487
Hazrati NRicci F(2023)Choice models and recommender systems effects on users’ choicesUser Modeling and User-Adapted Interaction10.1007/s11257-023-09366-x34:1(109-145)Online publication date: 18-May-2023
https://doi.org/10.1007/s11257-023-09366-x
Show More Cited By

Index Terms

Simulating the Impact of Recommender Systems on the Evolution of Collective Users' Choices
1. Information systems
  1. Information systems applications

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Published In

cover image ACM Conferences

HT '20: Proceedings of the 31st ACM Conference on Hypertext and Social Media

July 2020

327 pages

ISBN:9781450370981

DOI:10.1145/3372923

General Chairs:
Anastasia Salter
University of Central Florida, USA
,
John F. Barber
Washington State University Vancouver, USA
,
Program Chair:
Ujwal Gadiraju
Delft University of Technology, The Netherlands

Copyright © 2020 ACM.

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: 13 July 2020

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HT '20: 31st ACM Conference on Hypertext and Social Media

July 13 - 15, 2020

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Overall Acceptance Rate 378 of 1,158 submissions, 33%

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Cited By

Rahdari BBrusilovsky PKveton B(2024)Towards Simulation-Based Evaluation of Recommender Systems with Carousel InterfacesACM Transactions on Recommender Systems10.1145/36437092:1(1-25)Online publication date: 30-Jan-2024
https://dl.acm.org/doi/10.1145/3643709
Porcaro LGómez ECastillo C(2023)Assessing the Impact of Music Recommendation Diversity on Listeners: A Longitudinal StudyACM Transactions on Recommender Systems10.1145/36084872:1(1-47)Online publication date: 12-Jul-2023
https://dl.acm.org/doi/10.1145/3608487
Hazrati NRicci F(2023)Choice models and recommender systems effects on users’ choicesUser Modeling and User-Adapted Interaction10.1007/s11257-023-09366-x34:1(109-145)Online publication date: 18-May-2023
https://doi.org/10.1007/s11257-023-09366-x
Patro GPorcaro LMitchell LZhang QZehlike MGarg N(2022)Fair ranking: a critical review, challenges, and future directionsProceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency10.1145/3531146.3533238(1929-1942)Online publication date: 21-Jun-2022
https://dl.acm.org/doi/10.1145/3531146.3533238
Hazrati NRicci FBellogin ABoratto LSantos OArdissono LKnijnenburg B(2022)Simulating Users’ Interactions with Recommender SystemsAdjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3511047.3536402(95-98)Online publication date: 4-Jul-2022
https://dl.acm.org/doi/10.1145/3511047.3536402
Hazrati NRicci F(2022)Recommender systems effect on the evolution of users’ choices distributionInformation Processing and Management: an International Journal10.1016/j.ipm.2021.10276659:1Online publication date: 9-Apr-2022
https://dl.acm.org/doi/10.1016/j.ipm.2021.102766
Hazrati NDignum FLomuscio AEndriss UNowé A(2021)Impact of Recommender Systems on the Dynamics of Users' ChoicesProceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3463952.3464247(1811-1813)Online publication date: 3-May-2021
https://dl.acm.org/doi/10.5555/3463952.3464247
Jannach DZanker M(2012)Value and Impact of Recommender SystemsRecommender Systems Handbook10.1007/978-1-0716-2197-4_14(519-546)Online publication date: 24-Feb-2012
https://doi.org/10.1007/978-1-0716-2197-4_14

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