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Towards Psychologically-Grounded Dynamic Preference Models

Authors:

Mihaela Curmei,

Andreas A. Haupt,

Benjamin Recht,

Dylan Hadfield-MenellAuthors Info & Claims

RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

Pages 35 - 48

https://doi.org/10.1145/3523227.3546778

Published: 13 September 2022 Publication History

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Abstract

Designing recommendation systems that serve content aligned with time varying preferences requires proper accounting of the feedback effects of recommendations on human behavior and psychological condition. We argue that modeling the influence of recommendations on people’s preferences must be grounded in psychologically plausible models. We contribute a methodology for developing grounded dynamic preference models. We demonstrate this method with models that capture three classic effects from the psychology literature: Mere-Exposure, Operant Conditioning, and Hedonic Adaptation. We conduct simulation-based studies to show that the psychological models manifest distinct behaviors that can inform system design. Our study has two direct implications for dynamic user modeling in recommendation systems. First, the methodology we outline is broadly applicable for psychologically grounding dynamic preference models. It allows us to critique recent contributions based on their limited discussion of psychological foundation and their implausible predictions. Second, we discuss implications of dynamic preference models for recommendation systems evaluation and design. In an example, we show that engagement and diversity metrics may be unable to capture desirable recommendation system performance.

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Index Terms

Towards Psychologically-Grounded Dynamic Preference Models
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User models
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
    2. Users and interactive retrieval
      1. Personalization

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cover image ACM Other conferences

RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

September 2022

743 pages

ISBN:9781450392785

DOI:10.1145/3523227

Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

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Published: 13 September 2022

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RecSys '22

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RecSys '22: Sixteenth ACM Conference on Recommender Systems

September 18 - 23, 2022

WA, Seattle, USA

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

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https://dl.acm.org/doi/10.5555/3692070.3692292
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
Matias IFarhat-ul-Ain Akhmetzyanova DTomberg VBoratto LGena CMarras MGermanakos PPopescus E(2024)Modelling Users for User Modelling: Dynamic Personas for Improved Personalisation in Digital Behaviour ChangeAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665241(445-451)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3665241
Bi SWang WPan HFeng FHe XChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Proactive Recommendation with Iterative Preference GuidanceCompanion Proceedings of the ACM on Web Conference 202410.1145/3589335.3651548(871-874)Online publication date: 13-May-2024
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Tkach YShyian AVasylieva ODiuba I(2024)Method of Effective Counteraction Against Negative Information-Psychological Impact on the Regional Community by Using Social NetworksMathematical Modeling and Simulation of Systems10.1007/978-3-031-67348-1_18(239-251)Online publication date: 7-Sep-2024
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Saig ERosenfeld NKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Learning to suggest breaksProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619640(29671-29696)Online publication date: 23-Jul-2023
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Stray J(2023)The AI Learns to Lie to Please You: Preventing Biased Feedback Loops in Machine-Assisted Intelligence AnalysisAnalytics10.3390/analytics20200202:2(350-358)Online publication date: 18-Apr-2023
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Carroll MChan AAshton HKrueger D(2023)Characterizing Manipulation from AI SystemsProceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization10.1145/3617694.3623226(1-13)Online publication date: 30-Oct-2023
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Sguerra BTran VHennequin R(2023)Ex2Vec: Characterizing Users and Items from the Mere Exposure EffectProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608856(971-977)Online publication date: 14-Sep-2023
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