research-article

Recommender systems effect on the evolution of users’ choices distribution

Authors:

Naieme Hazrati,

Francesco RicciAuthors Info & Claims

Volume 59, Issue 1

https://doi.org/10.1016/j.ipm.2021.102766

Published: 01 January 2022 Publication History

Abstract

Recommender systems’ (RSs) research has mostly focused on algorithms aimed at improving platform owners’ revenues and user’s satisfaction. However, RSs have additional effects, which are related to their impact on users’ choices. In order to avoid an undesired system behaviour and anticipate the effects of an RS, the literature suggests employing simulations.

In this article we present a novel, well grounded and flexible simulation framework. We adopt a stochastic user’s choice model and simulate users’ repeated choices for items in the presence of alternative RSs. Properties of the simulated choices, such as their diversity and their quality, are analysed. We state four research questions, also motivated by identified research gaps, which are addressed by conducting an experimental study where three different data sets and five alternative RSs are used. We identify some important effects of RSs. We find that non-personalised RSs result in choices for items that have a larger predicted rating compared to personalised RSs. Moreover, when a user’s awareness set, which is the set containing the items that she can choose from, increases, then choices are more diverse, but the average quality (rating) of the choices decreases. Additionally, in order to achieve a higher choice diversity, increasing the awareness of the users is shown to be a more effective remedy than increasing the number of recommendations offered to the users.

Highlights

•

Simulating users’ choices under the influence of recommender systems.

•

Modelling the effect of recommender systems on users’ choice behaviour.

•

Simulating five alternative recommender systems.

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Modelling users’ awareness about the catalogue of items.

•

Estimating the utility for the choice model with an unbiased rating prediction model.

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Cited By

Carroll MFoote DSiththaranjan ARussell SDragan ASalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)AI alignment with changing and influenceable reward functionsProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692292(5706-5756)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692292
Wang LZhang JYang HChen ZTang JZhang ZChen XLin YSun HSong RZhao XXu JDou ZWang JWen J(2024)User Behavior Simulation with Large Language Model-based AgentsACM Transactions on Information Systems10.1145/370898543:2(1-37)Online publication date: 20-Dec-2024
https://dl.acm.org/doi/10.1145/3708985
Ferraro AEkstrand MBauer C(2024)It's Not You, It's Me: The Impact of Choice Models and Ranking Strategies on Gender Imbalance in Music RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688163(884-889)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688163
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Index Terms

Recommender systems effect on the evolution of users’ choices distribution
1. Computing methodologies
  1. Modeling and simulation
2. Information systems

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

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Published In

cover image Information Processing and Management: an International Journal

Information Processing and Management: an International Journal Volume 59, Issue 1

Jan 2022

821 pages

ISSN:0306-4573

Issue’s Table of Contents

Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 January 2022

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Cited By

Carroll MFoote DSiththaranjan ARussell SDragan ASalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)AI alignment with changing and influenceable reward functionsProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692292(5706-5756)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692292
Wang LZhang JYang HChen ZTang JZhang ZChen XLin YSun HSong RZhao XXu JDou ZWang JWen J(2024)User Behavior Simulation with Large Language Model-based AgentsACM Transactions on Information Systems10.1145/370898543:2(1-37)Online publication date: 20-Dec-2024
https://dl.acm.org/doi/10.1145/3708985
Ferraro AEkstrand MBauer C(2024)It's Not You, It's Me: The Impact of Choice Models and Ranking Strategies on Gender Imbalance in Music RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688163(884-889)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688163
Coppolillo EManco GGionis ABaeza-Yates RBonchi F(2024)Relevance Meets Diversity: A User-Centric Framework for Knowledge Exploration Through RecommendationsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671949(490-501)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671949
Chee JKalyanaraman SErnala SWeinsberg UDean SIoannidis SBaeza-Yates RBonchi F(2024)Harm Mitigation in Recommender Systems under User Preference DynamicsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671925(255-265)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671925
An GSun JYang YSun F(2024)Enhancing Collaborative Information with Contrastive Learning for Session-based RecommendationInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10373861:4Online publication date: 18-Jul-2024
https://dl.acm.org/doi/10.1016/j.ipm.2024.103738
Hazrati NRicci F(2024)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: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s11257-023-09366-x
Cai YWang F(2024)Exploring the Behavior of Users “Training” Douyin’s Personalized Recommendation Algorithm System in ChinaHuman Interface and the Management of Information10.1007/978-3-031-60114-9_14(189-208)Online publication date: 29-Jun-2024
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Wang SZhang XWang YRicci F(2023)Trustworthy Recommender SystemsACM Transactions on Intelligent Systems and Technology10.1145/362782615:4(1-20)Online publication date: 13-Oct-2023
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Cavenaghi ESottocornola GStella FZanker M(2023)A Systematic Study on Reproducibility of Reinforcement Learning in Recommendation SystemsACM Transactions on Recommender Systems10.1145/35965191:3(1-23)Online publication date: 14-Jul-2023
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