poster

Escape the bubble: guided exploration of music preferences for serendipity and novelty

Authors:

Maria Taramigkou,

Efthimios Bothos,

Konstantinos Christidis,

Dimitris Apostolou,

Gregoris MentzasAuthors Info & Claims

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

Pages 335 - 338

https://doi.org/10.1145/2507157.2507223

Published: 12 October 2013 Publication History

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Abstract

In order to predict user behaviour recommender systems generate views of the world according to expressed and known user preferences resulting in 'filter bubbles'. This kind of bubbles generally help users to easily identify objects they like. However, it is becoming increasingly difficult for users to escape their personalized world and change their perspectives especially in domains such as music. In this work we present a methodology and related system that allows users to initiate explorations of music genres by taking a gradual path towards the desired genre while viewing the preferences of other users. The proposed methodology is based on identifying 'latent genres' and using user preference graphs for detecting optimal paths towards a selected target latent genre. In this process we generate suggestions of artists a user should listen to, aiming towards serendipitous and novel encounters. We have implemented our approach in a music recommendation system and evaluated it with encouraging results.

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Cited By

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Yan CPu KLuo X(2024)Knowledge mapping of information cocoons: A bibliometric study using visual analysisJournal of Librarianship and Information Science10.1177/09610006231222628Online publication date: 17-Jan-2024
https://doi.org/10.1177/09610006231222628
Tommasel ADiaz-Pace J(2024)Leveraging Monte Carlo Tree Search for Group RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691713(1136-1141)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691713
Kotkov DMedlar AKask TGlowacka D(2024)The Dark Matter of Serendipity in Recommender SystemsProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638342(108-118)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638342
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Index Terms

Escape the bubble: guided exploration of music preferences for serendipity and novelty
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
    2. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

October 2013

516 pages

ISBN:9781450324090

DOI:10.1145/2507157

General Chairs:
Qiang Yang
Huawei Noah Ark Research Lab, China
,
Irwin King
Chinese University of Hong Kong, China
,
Qing Li
City University of Hong Kong, China
,
Program Chairs:
Pearl Pu
Ecole Polytechnique Fédérale de Lausanne, Switzerland
,
George Karypis
University of Minnesota, USA

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 ACM 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: 12 October 2013

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RecSys '13

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SIGCHI

RecSys '13: Seventh ACM Conference on Recommender Systems

October 12 - 16, 2013

Hong Kong, China

Acceptance Rates

RecSys '13 Paper Acceptance Rate 32 of 136 submissions, 24%;

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

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Cited By

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Yan CPu KLuo X(2024)Knowledge mapping of information cocoons: A bibliometric study using visual analysisJournal of Librarianship and Information Science10.1177/09610006231222628Online publication date: 17-Jan-2024
https://doi.org/10.1177/09610006231222628
Tommasel ADiaz-Pace J(2024)Leveraging Monte Carlo Tree Search for Group RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691713(1136-1141)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691713
Kotkov DMedlar AKask TGlowacka D(2024)The Dark Matter of Serendipity in Recommender SystemsProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638342(108-118)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638342
Sharma NLiao QXiao Z(2024)Generative Echo Chamber? Effect of LLM-Powered Search Systems on Diverse Information SeekingProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642459(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642459
TSUKUDA KISHIDA KHAMASAKI MGOTO M(2023)Kiite Cafe: A Web Service Enabling Users to Listen to the Same Song at the Same Moment While Reacting to the SongIEICE Transactions on Information and Systems10.1587/transinf.2023EDP7001E106.D:11(1906-1915)Online publication date: 1-Nov-2023
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Jin YChen LCai WZhao X(2023)CRS-Que: A User-Centric Evaluation Framework for Conversational Recommender SystemsACM Transactions on Recommender Systems10.1145/3631534Online publication date: 2-Nov-2023
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Maccatrozzo VKuhn TCeolin DVan Ossenbruggen J(2023)The Role of Serendipity in User-Curated Music PlaylistsProceedings of the 12th Knowledge Capture Conference 202310.1145/3587259.3627552(140-147)Online publication date: 5-Dec-2023
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Liang YWillemsen M(2022)Exploring the longitudinal effects of nudging on users’ music genre exploration behavior and listening preferencesProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3546772(3-13)Online publication date: 12-Sep-2022
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Tommasel ARodriguez JGodoy DBellogin ABoratto LSantos OArdissono LKnijnenburg B(2022)Haven’t I just Listened to This?: Exploring Diversity in Music RecommendationsAdjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3511047.3536409(35-40)Online publication date: 4-Jul-2022
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Petridis SDaskalova NMennicken SWay SLamere PThom J(2022)TastePaths: Enabling Deeper Exploration and Understanding of Personal Preferences in Recommender SystemsProceedings of the 27th International Conference on Intelligent User Interfaces10.1145/3490099.3511156(120-133)Online publication date: 22-Mar-2022
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