extended-abstract

Sequences of Diverse Song Recommendations: An Exploratory Study in a Commercial System

Authors:

Nava Tintarev,

Christoph Lofi,

Cynthia C.S. LiemAuthors Info & Claims

UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

Pages 391 - 392

https://doi.org/10.1145/3079628.3079633

Published: 09 July 2017 Publication History

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Abstract

This paper presents an exploratory study of the perceptions users have of diversity and ordering in playlist recommendations. There is a match between the diversification approach used in the system, and importance that users placed on the item properties. Surprisingly, participants had no expectations of the songs being in a particular order in a playlist. We discuss possible explanations for this finding, refining the research agenda to consider which ordering choices are perceptible to users, and influence user satisfaction.

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Cited By

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Porcaro LGomez ECastillo CElsweiler DKruschwitz ULudwig B(2022)Diversity in the Music Listening Experience: Insights from Focus Group InterviewsProceedings of the 2022 Conference on Human Information Interaction and Retrieval10.1145/3498366.3505778(272-276)Online publication date: 14-Mar-2022
https://dl.acm.org/doi/10.1145/3498366.3505778
Schedl M(2019)Deep Learning in Music Recommendation SystemsFrontiers in Applied Mathematics and Statistics10.3389/fams.2019.000445Online publication date: 29-Aug-2019
https://doi.org/10.3389/fams.2019.00044
Zamani HSchedl MLamere PChen C(2019)An Analysis of Approaches Taken in the ACM RecSys Challenge 2018 for Automatic Music Playlist ContinuationACM Transactions on Intelligent Systems and Technology10.1145/334425710:5(1-21)Online publication date: 18-Sep-2019
https://dl.acm.org/doi/10.1145/3344257
Show More Cited By

Index Terms

Sequences of Diverse Song Recommendations: An Exploratory Study in a Commercial System
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

Recommendations

Updatable, Accurate, Diverse, and Scalable Recommendations for Interactive Applications

Recommender systems form the backbone of many interactive systems. They incorporate user feedback to personalize the user experience typically via personalized recommendation lists. As users interact with a system, an increasing amount of data about a ...
A framework for diversifying recommendation lists by user interest expansion

Recommender systems have been widely used to discover users' preferences and recommend interesting items to users during this age of information overload. Researchers in the field of recommender systems have realized that the quality of a top-N ...
An analysis of users' propensity toward diversity in recommendations
RecSys '14: Proceedings of the 8th ACM Conference on Recommender systems

Providing very accurate recommendations to end users has been nowadays recognized to be just one of the main tasks a recommender systems must be able to perform. While predicting relevant suggestions, attention needs to be paid to their diversification ...

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Published In

UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

July 2017

420 pages

ISBN:9781450346351

DOI:10.1145/3079628

General Chairs:
Maria Bielikova
Slovak University of Technology in Bratislava, Slovakia
,
Eelco Herder
L3S Research Center, Germany and Radboud Universiteit Nijmegen, The Netherlands
,
Program Chairs:
Federica Cena
University of Torino, Italy
,
Michel Desmarais
Polytechnique University, Montreal, Canada

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

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Published: 09 July 2017

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UMAP '17

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UMAP '17: 25th Conference on User Modeling, Adaptation and Personalization

July 9 - 12, 2017

Bratislava, Slovakia

Acceptance Rates

UMAP '17 Paper Acceptance Rate 29 of 80 submissions, 36%;

Overall Acceptance Rate 162 of 633 submissions, 26%

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UMAP '25

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33rd ACM Conference on User Modeling, Adaptation and Personalization

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Porcaro LGomez ECastillo CElsweiler DKruschwitz ULudwig B(2022)Diversity in the Music Listening Experience: Insights from Focus Group InterviewsProceedings of the 2022 Conference on Human Information Interaction and Retrieval10.1145/3498366.3505778(272-276)Online publication date: 14-Mar-2022
https://dl.acm.org/doi/10.1145/3498366.3505778
Schedl M(2019)Deep Learning in Music Recommendation SystemsFrontiers in Applied Mathematics and Statistics10.3389/fams.2019.000445Online publication date: 29-Aug-2019
https://doi.org/10.3389/fams.2019.00044
Zamani HSchedl MLamere PChen C(2019)An Analysis of Approaches Taken in the ACM RecSys Challenge 2018 for Automatic Music Playlist ContinuationACM Transactions on Intelligent Systems and Technology10.1145/334425710:5(1-21)Online publication date: 18-Sep-2019
https://dl.acm.org/doi/10.1145/3344257
Kamehkhosh IBonnin GJannach D(2019)Effects of recommendations on the playlist creation behavior of usersUser Modeling and User-Adapted Interaction10.1007/s11257-019-09237-4Online publication date: 22-May-2019
https://doi.org/10.1007/s11257-019-09237-4
Osche PCastagnos SBoyer A(2019)AntRS: Recommending Lists Through a Multi-objective Ant Colony SystemAdvances in Information Retrieval10.1007/978-3-030-15712-8_15(229-243)Online publication date: 7-Apr-2019
https://doi.org/10.1007/978-3-030-15712-8_15
Kaya MBridge D(2018)Automatic Playlist Continuation using Subprofile-Aware DiversificationProceedings of the ACM Recommender Systems Challenge 201810.1145/3267471.3267472(1-6)Online publication date: 2-Oct-2018
https://dl.acm.org/doi/10.1145/3267471.3267472
Schedl MZamani HChen CDeldjoo YElahi M(2018)Current challenges and visions in music recommender systems researchInternational Journal of Multimedia Information Retrieval10.1007/s13735-018-0154-27:2(95-116)Online publication date: 5-Apr-2018
https://doi.org/10.1007/s13735-018-0154-2

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Abstract

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Cited By

Index Terms

Recommendations

Updatable, Accurate, Diverse, and Scalable Recommendations for Interactive Applications

A framework for diversifying recommendation lists by user interest expansion

An analysis of users' propensity toward diversity in recommendations

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