extended-abstract

Testing a recommender system for self-actualization

Author:

Daricia WilkinsonAuthors Info & Claims

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

Pages 543 - 547

https://doi.org/10.1145/3240323.3240324

Published: 27 September 2018 Publication History

Abstract

Traditionally, recommender systems are built with the goal of aiding users' decision-making process by extrapolating what they like and what they have done to predict what they want next. However, in attempting to personalize the suggestions to users' preferences, these systems create an isolated universe of information for each user, which may limit their perspectives and promote complacency. In this paper, we describe our research plan to test a novel approach to recommender systems that goes beyond "good recommendations" that supports user aspirations and exploration.

References

[1]

Dirk Bollen, Bart P. Knijnenburg, Martijn C. Willemsen, and Mark Graus. 2010. Understanding choice overload in recommender systems. In Proceedings of the fourth ACM conference on Recommender systems, 63--70.

Digital Library

[2]

Li Chen and Pearl Pu. 2009. Interaction design guidelines on critiquing-based recommender systems. User Modeling and User-Adapted Interaction 19, 3: 167--206.

Digital Library

[3]

Li Chen and Pearl Pu. 2012. Critiquing-based recommenders: survey and emerging trends. User Modeling and User-Adapted Interaction 22, 1--2: 125--150.

Digital Library

[4]

Gerhard Friedrich and Markus Zanker. 2011. A Taxonomy for Generating Explanations in Recommender Systems. AI Magazine 32, 3: 90--98.

Digital Library

[5]

Fatih Gedikli, Dietmar Jannach, and Mouzhi Ge. 2014. How should I explain? A comparison of different explanation types for recommender systems. International Journal of Human-Computer Studies 72, 4: 367--382.

Digital Library

[6]

Chen He, Denis Parra, and Katrien Verbert. 2016. Interactive recommender systems: a survey of the state of the art and future research challenges and opportunities. Expert Systems with Applications 56: 9--27.

Digital Library

[7]

Bart P. Knijnenburg, Svetlin Bostandjiev, John O'Donovan, and Alfred Kobsa. 2012. Inspectability and control in social recommenders. In Proceedings of the sixth ACM conference on Recommender systems (RecSys '12), 43--50.

Digital Library

[8]

Bart P. Knijnenburg, Niels J.M. Reijmer, and Martijn C. Willemsen. 2011. Each to his own: how different users call for different interaction methods in recommender systems. In Proceedings of the fifth ACM conference on Recommender systems, 141--148.

Digital Library

[9]

Bart P. Knijnenburg, Saadhika Sivakumar, and Daricia Wilkinson. 2016. Recommender systems for self-actualization. In Proceedings of the 10th ACM Conference on Recommender Systems, 11--14.

Digital Library

[10]

Bart P. Knijnenburg, Martijn C. Willemsen, Zeno Gantner, Hakan Soncu, and Chris Newell. 2012. Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction 22, 4--5: 441--504.

Digital Library

[11]

Alfred Kobsa, Bart P. Knijnenburg, and Benjamin Livshits. 2014. Let's Do It at My Place Instead?: Attitudinal and Behavioral Study of Privacy in Client-side Personalization. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '14), 81--90.

Digital Library

[12]

Joseph A. Konstan and John Riedl. 2012. Recommender systems: from algorithms to user experience. User Modeling and User-Adapted Interaction 22, 1--2: 101--123.

Digital Library

[13]

Jaron Lanier. 2010. You Are Not a Gadget: A Manifesto. Thorndike Press, Waterville, Me.

Digital Library

[14]

Eli Pariser. 2012. The filter bubble: how the new personalized Web is changing what we read and how we think. Penguin Books, New York, N.Y.

Digital Library

[15]

Pearl Pu, Boi Faltings, Li Chen, Jiyong Zhang, and Paolo Viappiani. 2011. Usability Guidelines for Product Recommenders Based on Example Critiquing Research. In Recommender Systems Handbook, Francesco Ricci, Lior Rokach, Bracha Shapira and Paul B. Kantor (eds.). Springer US, Boston, MA, 511--545. Retrieved May 4, 2011 from

[16]

P. Resnick, N. Iacovou, M. Sushak, P. Bergstrom, and J. Riedl. 1994. GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In ACM Conference on Computer Supported Cooperative Work, 175--186.

Digital Library

[17]

Paul Resnick, R Kelly Garrett, Travis Kriplean, Sean A Munson, and Natalie Jomini Stroud. 2013. Bursting your (filter) bubble: strategies for promoting diverse exposure. In Proceedings of the 2013 conference on Computer supported cooperative work companion, 95--100.

Digital Library

[18]

Andrew I. Schein, Alexandrin Popescul, Lyle H. Ungar, and David M. Pennock. 2002. Methods and Metrics for Cold-start Recommendations. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '02), 253--260.

Digital Library

[19]

Martijn C. Willemsen, Mark P. Graus, and Bart P. Knijnenburg. 2016. Understanding the role of latent feature diversification on choice difficulty and satisfaction. User Modeling and User-Adapted Interaction 26, 4: 347--389.

Digital Library

[20]

Cai-Nicolas Ziegler, Sean M. McNee, Joseph A. Konstan, and Georg Lausen. 2005. Improving recommendation lists through topic diversification. In Proceedings of the 14th international conference on World Wide Web, 22.

Digital Library

[21]

MovieLens. Retrieved May 31, 2018 from https://movielens.org/

Cited By

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
Guesmi MSiepmann CChatti MJoarder SAin QAlatrash R(2023)Validation of the EDUSS Framework for Self-Actualization Based on Transparent User Models: A Qualitative StudyAdjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3563359.3597379(229-238)Online publication date: 26-Jun-2023
https://dl.acm.org/doi/10.1145/3563359.3597379
Valtolina SMatamoros R(2023)EUD Strategy in the Education Field for Supporting Teachers in Creating Digital CoursesEnd-User Development10.1007/978-3-031-34433-6_17(250-267)Online publication date: 30-May-2023
https://doi.org/10.1007/978-3-031-34433-6_17
Show More Cited By

Index Terms

Testing a recommender system for self-actualization
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User studies
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

Recommendations

Recommender Systems for Self-Actualization
RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems

Every day, we are confronted with an abundance of decisions that require us to choose from a seemingly endless number of choice options. Recommender systems are supposed to help us deal with this formidable task, but some scholars claim that these ...
Beyond the top-N: algorithms that generate recommendations for self-actualization
RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems

Recommender systems traditionally provide users with recommendations that match their preferences, which creates a personalized user experience and increases users' satisfaction. However, recommendations from traditional systems may sometimes be ...
Exploiting Social Tagging in a Web 2.0 Recommender System

Recommender systems help users cope with information overload by using their preferences to recommend items. To date, most recommenders have employed users' ratings, information about the user's profile, or metadata describing the items. To take ...

Comments

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.

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 September 2018

Check for updates

Author Tags

Qualifiers

Extended-abstract

Conference

RecSys '18

Sponsor:

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%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
355
Total Downloads

Downloads (Last 12 months)10
Downloads (Last 6 weeks)0

Reflects downloads up to 17 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

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
Guesmi MSiepmann CChatti MJoarder SAin QAlatrash R(2023)Validation of the EDUSS Framework for Self-Actualization Based on Transparent User Models: A Qualitative StudyAdjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3563359.3597379(229-238)Online publication date: 26-Jun-2023
https://dl.acm.org/doi/10.1145/3563359.3597379
Valtolina SMatamoros R(2023)EUD Strategy in the Education Field for Supporting Teachers in Creating Digital CoursesEnd-User Development10.1007/978-3-031-34433-6_17(250-267)Online publication date: 30-May-2023
https://doi.org/10.1007/978-3-031-34433-6_17
Guesmi MChatti MTayyar AAin QJoarder S(2022)Interactive Visualizations of Transparent User Models for Self-Actualization: A Human-Centered Design ApproachMultimodal Technologies and Interaction10.3390/mti60600426:6(42)Online publication date: 30-May-2022
https://doi.org/10.3390/mti6060042
Valtolina SMatamoros RMusiu EEpifania FVilla MBottoni PPanizzi E(2022)Extended UTAUT model to analyze the acceptance of virtual assistant’s recommendations using interactive visualisationsProceedings of the 2022 International Conference on Advanced Visual Interfaces10.1145/3531073.3531129(1-5)Online publication date: 6-Jun-2022
https://dl.acm.org/doi/10.1145/3531073.3531129
Chu SBrown SPark HSpornhauer B(2020)Towards Personalized Movie Selection for Wellness: Investigating Event-Inspired MoviesInternational Journal of Human–Computer Interaction10.1080/10447318.2020.1768665(1-13)Online publication date: 25-May-2020
https://doi.org/10.1080/10447318.2020.1768665
Srivastava ABala PKumar B(2019)New perspectives on gray sheep behavior in E-commerce recommendationsJournal of Retailing and Consumer Services10.1016/j.jretconser.2019.02.018Online publication date: Mar-2019
https://doi.org/10.1016/j.jretconser.2019.02.018
Guo LPera SEkstrand MAmatriain XO'Donovan J(2018)Beyond the top-NProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3240330(573-577)Online publication date: 27-Sep-2018
https://dl.acm.org/doi/10.1145/3240323.3240330

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents