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Behaviorism is Not Enough: Better Recommendations through Listening to Users

Authors:

Michael D. Ekstrand,

Martijn C. WillemsenAuthors Info & Claims

RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems

Pages 221 - 224

https://doi.org/10.1145/2959100.2959179

Published: 07 September 2016 Publication History

Abstract

Behaviorism is the currently-dominant paradigm for building and evaluating recommender systems. Both the operation and the evaluation of recommender system applications are most often driven by analyzing the behavior of users. In this paper, we argue that listening to what users say about the items and recommendations they like, the control they wish to exert on the output, and the ways in which they perceive the system and not just observing what they do will enable important developments in the future of recommender systems. We provide both philosophical and pragmatic motivations for this idea, describe the various points in the recommendation and evaluation processes where explicit user input may be considered, and discuss benefits that may result from considered incorporation of user preferences at each of these points. In particular, we envision recommender applications that aim to support users' better selves: helping them live the life that they desire to lead. For example, recommender-assisted behavior change requires algorithms to predict not what users choose or do now, inferable from behavioral data, but what they should choose or do in the future to become healthier, fitter, more sustainable, or culturally aware. We hope that our work will spur useful discussion and many new ideas for recommenders that empower their users.

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Cited By

Leix Palumbo DPrey R(2024)Sounding out voice biometrics: Comparing and contrasting how the state and the private sector determine identity through voiceBig Data & Society10.1177/2053951724129788911:4Online publication date: 14-Nov-2024
https://doi.org/10.1177/20539517241297889
Khan SKnijnenburg BDixon E(2024)Evaluating ADHD Users’ Experience with Recommender SystemsCompanion Proceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640544.3645222(84-88)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3640544.3645222
Besbes OKanoria YKumar A(2024)The Fault in Our Recommendations: On the Perils of Optimizing the MeasurableProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688144(200-208)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688144
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Index Terms

Behaviorism is Not Enough: Better Recommendations through Listening to Users
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User studies
  2. Interaction design
    1. Interaction design process and methods
      1. Participatory design
      2. User centered design
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Conferences

RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems

September 2016

490 pages

ISBN:9781450340359

DOI:10.1145/2959100

General Chairs:
Shilad Sen
Macalester College, USA
,
Werner Geyer
IBM Research Cambridge, USA
,
Program Chairs:
Jill Freyne
CSIRO, Australia
,
Pablo Castells
Universidad Autónoma de Madrid, Spain

Copyright © 2016 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 the author(s) 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 September 2016

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RecSys '16

Sponsor:

SIGCHI

RecSys '16: Tenth ACM Conference on Recommender Systems

September 15 - 19, 2016

Massachusetts, Boston, USA

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RecSys '16 Paper Acceptance Rate 29 of 159 submissions, 18%;

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

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Cited By

Leix Palumbo DPrey R(2024)Sounding out voice biometrics: Comparing and contrasting how the state and the private sector determine identity through voiceBig Data & Society10.1177/2053951724129788911:4Online publication date: 14-Nov-2024
https://doi.org/10.1177/20539517241297889
Khan SKnijnenburg BDixon E(2024)Evaluating ADHD Users’ Experience with Recommender SystemsCompanion Proceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640544.3645222(84-88)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3640544.3645222
Besbes OKanoria YKumar A(2024)The Fault in Our Recommendations: On the Perils of Optimizing the MeasurableProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688144(200-208)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688144
Binst B(2024)How to Evaluate Serendipity in Recommender Systems: the Need for a SerendiptionnaireProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688017(1335-1341)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688017
Knijnenburg BMalthouse E(2024)Conducting User Experiments in Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3687090(1272-1273)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3687090
Gómez-Zará DLiu YNeves LShah NBos M(2024)Unpacking the exploration–exploitation tradeoff on SnapchatComputers in Human Behavior10.1016/j.chb.2023.108014150:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.chb.2023.108014
Bouchaud P(2024)Skewed perspectives: examining the influence of engagement maximization on content diversity in social media feedsJournal of Computational Social Science10.1007/s42001-024-00255-w7:1(721-739)Online publication date: 20-Mar-2024
https://doi.org/10.1007/s42001-024-00255-w
Starke AWillemsen M(2024)Psychologically Informed Design of Energy Recommender Systems: Are Nudges Still Effective in Tailored Choice Environments?A Human-Centered Perspective of Intelligent Personalized Environments and Systems10.1007/978-3-031-55109-3_9(221-259)Online publication date: 1-May-2024
https://doi.org/10.1007/978-3-031-55109-3_9
Torkamaan HTahaei MBuijsman SXiao ZWilkinson DKnijnenburg B(2024)The Role of Human-Centered AI in User Modeling, Adaptation, and Personalization—Models, Frameworks, and ParadigmsA Human-Centered Perspective of Intelligent Personalized Environments and Systems10.1007/978-3-031-55109-3_2(43-84)Online publication date: 1-May-2024
https://doi.org/10.1007/978-3-031-55109-3_2
Pera MCena FLandoni MMusto CStarke A(2024)Human Factors in User Modeling for Intelligent SystemsA Human-Centered Perspective of Intelligent Personalized Environments and Systems10.1007/978-3-031-55109-3_1(3-42)Online publication date: 1-May-2024
https://doi.org/10.1007/978-3-031-55109-3_1
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