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Music recommendations for groups of users

Published: 22 October 2013 Publication History

Abstract

This paper presents an algorithm capable of providing meaningful recommendations to small sets of users. We consider not only rating patterns, bias tendencies, and temporal fluctuations, but also group-leaders. The approach here presented intends to bring a fresh new look over group recommendations, making use of latent factor space to identify groups and make recommendations. Although these recommendations are oriented towards a few users, the preferences of their respective group leaders (users that better represent the group) are also taken into account to diversify and smooth these recommendations. In contrast to the majority of group recommender systems described in literature, our system employs a collaborative filtering approach based on latent factor space instead of content-based or ratings merging approaches.

References

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A. Crossen, J. Budzik, and K. J. Hammond. Flytrap: intelligent group music recommendation. In International Conference on Intelligent User Interfaces, IUI '02, pages 184--185, New York, NY, USA, 2002.
[2]
A. Jameson and B. Smyth. The adaptive web. chapter Recommendation to groups, pages 596--627. Springer-Verlag, Berlin, Heidelberg, 2007.
[3]
T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu. An efficient k-means clustering algorithm: Analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell., 24:881--892, July 2002.
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T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu. A local search approximation algorithm for k-means clustering. In Annual Symposium on Computational Geometry, SCG'02, pages 10--18, New York, NY, USA, 2002. ACM.
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Y. Koren. Collaborative filtering with temporal dynamics. Commun. ACM, 53:89--97, April 2010.
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H. Mahato, D. Kern, P. Holleis, and A. Schmidt. Implicit personalization of public environments using bluetooth. In CHI '08 extended abstracts on Human factors in computing systems, CHI EA '08, pages 3093--3098, New York, NY, USA, 2008. ACM.
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K. McCarthy, M. Salamo, L. Coyle, L. McGinty, B. Smyth, and P. Nixon. Cats: A synchronous approach to collaborative group recommendation. In International Florida Artificial Intelligence Research Society Conference, Melbourne Beach, Florida, USA, May 11-13, 2006, pages 86--91. AAAI Press, May 2006.

Cited By

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  • (2022)To Flip or Not to Flip: Conformity Effect Across CulturesCHI Conference on Human Factors in Computing Systems Extended Abstracts10.1145/3491101.3519662(1-7)Online publication date: 27-Apr-2022
  • (2021)Group Recommender Systems in the Music Domain: A Systematic Literature ReviewNew Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence10.1007/978-3-030-87687-6_28(296-307)Online publication date: 30-Oct-2021
  • (2021)What Songs We Listen to Together: Automatic Music Selection for GroupsRecommender Systems for Medicine and Music10.1007/978-3-030-66450-3_9(135-152)Online publication date: 8-Apr-2021
  • Show More Cited By

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  1. Music recommendations for groups of users

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    cover image ACM Conferences
    ImmersiveMe '13: Proceedings of the 2013 ACM international workshop on Immersive media experiences
    October 2013
    68 pages
    ISBN:9781450324021
    DOI:10.1145/2512142
    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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    New York, NY, United States

    Publication History

    Published: 22 October 2013

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    Author Tags

    1. collaborative-filtering
    2. groups
    3. recommendations

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    • Research-article

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    MM '13
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    MM '13: ACM Multimedia Conference
    October 22, 2013
    Barcelona, Spain

    Acceptance Rates

    ImmersiveMe '13 Paper Acceptance Rate 6 of 19 submissions, 32%;
    Overall Acceptance Rate 11 of 31 submissions, 35%

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    Cited By

    View all
    • (2022)To Flip or Not to Flip: Conformity Effect Across CulturesCHI Conference on Human Factors in Computing Systems Extended Abstracts10.1145/3491101.3519662(1-7)Online publication date: 27-Apr-2022
    • (2021)Group Recommender Systems in the Music Domain: A Systematic Literature ReviewNew Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence10.1007/978-3-030-87687-6_28(296-307)Online publication date: 30-Oct-2021
    • (2021)What Songs We Listen to Together: Automatic Music Selection for GroupsRecommender Systems for Medicine and Music10.1007/978-3-030-66450-3_9(135-152)Online publication date: 8-Apr-2021
    • (2020)Conformity Behavior in Group Playlist CreationExtended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems10.1145/3334480.3382942(1-10)Online publication date: 25-Apr-2020
    • (2015)Enhancing memory-based collaborative filtering for group recommender systemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2014.11.04242:7(3801-3812)Online publication date: 1-May-2015

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