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Beyond the top-N: algorithms that generate recommendations for self-actualization

Published: 27 September 2018 Publication History

Abstract

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 considered too personalized, which isolates users from a diversity of perspectives, content, and experiences, and thus make them less likely to discover new things. To overcome this drawback, we argue that recommenders should more actively keep the user "in-the-loop" by providing alternative recommendation lists that go beyond the traditional Top-N list. Such Recommender Systems for Self-Actualization follow a more holistic human-centered personalization practice by supporting users in developing, exploring and understanding their unique tastes and preferences. In this paper, we discuss a series of algorithms that generate four new recommendation lists. These lists enable the recommender to gain a more holistic view of the user and also allow the user to learn more about themselves.

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Cited By

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  • (2024)You Today, Better Tomorrow: Envisioning the Role of Conversation in Recommender Systems of the FutureProceedings of the 6th ACM Conference on Conversational User Interfaces10.1145/3640794.3665881(1-5)Online publication date: 8-Jul-2024
  • (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
  • (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
  • Show More Cited By

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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
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.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 September 2018

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Author Tags

  1. human-centered personalization
  2. over-personalized top-N recommendations
  3. recommender systems
  4. self-actualization

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  • Extended-abstract

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RecSys '18
Sponsor:
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%

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Cited By

View all
  • (2024)You Today, Better Tomorrow: Envisioning the Role of Conversation in Recommender Systems of the FutureProceedings of the 6th ACM Conference on Conversational User Interfaces10.1145/3640794.3665881(1-5)Online publication date: 8-Jul-2024
  • (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
  • (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
  • (2022)PSR: Probabilistic Serendipitous Recommendations2022 International Conference on Computational Science and Computational Intelligence (CSCI)10.1109/CSCI58124.2022.00144(790-795)Online publication date: Dec-2022
  • (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
  • (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

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