abstract

I've got 10 million songs in my pocket: now what?

Author:

Paul B. LamereAuthors Info & Claims

RecSys '12: Proceedings of the sixth ACM conference on Recommender systems

Pages 207 - 208

https://doi.org/10.1145/2365952.2365994

Published: 09 September 2012 Publication History

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Abstract

The proverbial celestial jukebox has become a reality. With today's online music services a music fan is never more than a few clicks away from being able to listen to nearly any song that has ever been recorded. Recommender systems can play a key role in this new music ecosystem, helping listeners explore, discover, organize and share music. However, in many ways music recommendation is very different than recommendation in other well-studied domains such as books and movies. In this talk we explore how recommender systems can be used in the music space, and the particular challenges that the music domain presents to the designers of recommender systems.

Cited By

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Pereira BUeda APenha GSantos RZiviani NBogers TSaid ABrusilovsky PTikk D(2019)Online learning to rank for sequential music recommendationProceedings of the 13th ACM Conference on Recommender Systems10.1145/3298689.3347019(237-245)Online publication date: 10-Sep-2019
https://dl.acm.org/doi/10.1145/3298689.3347019
Panniello UGorgoglione MTuzhilin A(2016)Research Note—In CARSs We Trust: How Context-Aware Recommendations Affect Customers’ Trust and Other Business Performance Measures of Recommender SystemsInformation Systems Research10.1287/isre.2015.061027:1(182-196)Online publication date: Mar-2016
https://doi.org/10.1287/isre.2015.0610
Amatriain XBasilico JSen SGeyer WFreyne JCastells P(2016)Past, Present, and Future of Recommender SystemsProceedings of the 10th ACM Conference on Recommender Systems10.1145/2959100.2959144(211-214)Online publication date: 7-Sep-2016
https://dl.acm.org/doi/10.1145/2959100.2959144
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Index Terms

I've got 10 million songs in my pocket: now what?
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Information

Published In

RecSys '12: Proceedings of the sixth ACM conference on Recommender systems

September 2012

376 pages

ISBN:9781450312707

DOI:10.1145/2365952

General Chairs:
Pádraig Cunningham
University College Dublin, Ireland
,
Neil Hurley
University College Dublin, Ireland
,
Program Chairs:
Ido Guy
IBM Haifa Research Laboratory, Israel
,
Sarabjot Singh Anand
University of Warwick, England

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: 09 September 2012

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Conference

RecSys '12

Sponsor:

SIGCHI

RecSys '12: Sixth ACM Conference on Recommender Systems

September 9 - 13, 2012

Dublin, Ireland

Acceptance Rates

RecSys '12 Paper Acceptance Rate 24 of 119 submissions, 20%;

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

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Cited By

View all

Pereira BUeda APenha GSantos RZiviani NBogers TSaid ABrusilovsky PTikk D(2019)Online learning to rank for sequential music recommendationProceedings of the 13th ACM Conference on Recommender Systems10.1145/3298689.3347019(237-245)Online publication date: 10-Sep-2019
https://dl.acm.org/doi/10.1145/3298689.3347019
Panniello UGorgoglione MTuzhilin A(2016)Research Note—In CARSs We Trust: How Context-Aware Recommendations Affect Customers’ Trust and Other Business Performance Measures of Recommender SystemsInformation Systems Research10.1287/isre.2015.061027:1(182-196)Online publication date: Mar-2016
https://doi.org/10.1287/isre.2015.0610
Amatriain XBasilico JSen SGeyer WFreyne JCastells P(2016)Past, Present, and Future of Recommender SystemsProceedings of the 10th ACM Conference on Recommender Systems10.1145/2959100.2959144(211-214)Online publication date: 7-Sep-2016
https://dl.acm.org/doi/10.1145/2959100.2959144
Jannach DKamehkhosh IBonnin GVassileva JBlustein JAroyo LD'Mello S(2016)Biases in Automated Music Playlist GenerationProceedings of the 2016 Conference on User Modeling Adaptation and Personalization10.1145/2930238.2930283(281-285)Online publication date: 13-Jul-2016
https://dl.acm.org/doi/10.1145/2930238.2930283
Amatriain XBasilico J(2015)Recommender Systems in Industry: A Netflix Case StudyRecommender Systems Handbook10.1007/978-1-4899-7637-6_11(385-419)Online publication date: 2015
https://doi.org/10.1007/978-1-4899-7637-6_11
Bonnin GJannach D(2014)Automated Generation of Music Playlists: Survey and ExperimentsACM Computing Surveys10.1145/265248147:2(1-35)Online publication date: 12-Nov-2014
https://dl.acm.org/doi/10.1145/2652481

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Recommendations

Examining the Relationship Between Songs and Psychological Characteristics

ChordRipple: Recommending Chords to Help Novice Composers Go Beyond the Ordinary

ChordRipple: Adaptively Recommending and Propagating Chord Changes for Songwriters