Article

A hybrid social-acoustic recommendation system for popular music

Author:

Justin DonaldsonAuthors Info & Claims

RecSys '07: Proceedings of the 2007 ACM conference on Recommender systems

Pages 187 - 190

https://doi.org/10.1145/1297231.1297271

Published: 19 October 2007 Publication History

Abstract

Recommendation systems leverage several types of information relating to a recommendable item. The recommendation methods are often based on the analysis of how a set of users associate or rate a given set of items, but they can also focus on the analysis of how the content of the items is related. This paper discusses a hybrid recommendation system for music - a system that leverages both spectral graph properties of an item-based collaborative filtering association network as well as acoustic features of the underlying music signal. Both features are balanced appropriately and used to disambiguate the music-seeking intentions of a user.

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Cited By

Paul AWu ZLiu KGong S(2022)Personalized recommendation: From clothing to academicMultimedia Tools and Applications10.1007/s11042-022-12259-781:10(14573-14588)Online publication date: 25-Feb-2022
https://doi.org/10.1007/s11042-022-12259-7
Gao ZGuo CMa SLiu X(2022)Improving Community Detection Performance in Heterogeneous Music Network by Learning Edge-Type Usefulness DistributionInformation for a Better World: Shaping the Global Future10.1007/978-3-030-96960-8_5(68-78)Online publication date: 23-Feb-2022
https://doi.org/10.1007/978-3-030-96960-8_5
Kowald DMuellner PZangerle EBauer CSchedl MLex E(2021)Support the underground: characteristics of beyond-mainstream music listenersEPJ Data Science10.1140/epjds/s13688-021-00268-910:1Online publication date: 30-Mar-2021
https://doi.org/10.1140/epjds/s13688-021-00268-9
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Index Terms

A hybrid social-acoustic recommendation system for popular music
1. Information systems
  1. Information retrieval
2. Theory of computation
  1. Semantics and reasoning
    1. Program reasoning
      1. Abstraction

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Published In

cover image ACM Conferences

RecSys '07: Proceedings of the 2007 ACM conference on Recommender systems

October 2007

222 pages

ISBN:9781595937308

DOI:10.1145/1297231

General Chair:
Joseph A. Konstan
University of Minnesota, USA
,
Program Chairs:
John Riedl
University of Minnesota, USA
,
Barry Smyth
University College, Dublin, Ireland

Copyright © 2007 ACM.

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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Publication History

Published: 19 October 2007

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RecSys07

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RecSys07: ACM Conference on Recommender Systems

October 19 - 20, 2007

MN, Minneapolis, USA

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

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Cited By

Paul AWu ZLiu KGong S(2022)Personalized recommendation: From clothing to academicMultimedia Tools and Applications10.1007/s11042-022-12259-781:10(14573-14588)Online publication date: 25-Feb-2022
https://doi.org/10.1007/s11042-022-12259-7
Gao ZGuo CMa SLiu X(2022)Improving Community Detection Performance in Heterogeneous Music Network by Learning Edge-Type Usefulness DistributionInformation for a Better World: Shaping the Global Future10.1007/978-3-030-96960-8_5(68-78)Online publication date: 23-Feb-2022
https://doi.org/10.1007/978-3-030-96960-8_5
Kowald DMuellner PZangerle EBauer CSchedl MLex E(2021)Support the underground: characteristics of beyond-mainstream music listenersEPJ Data Science10.1140/epjds/s13688-021-00268-910:1Online publication date: 30-Mar-2021
https://doi.org/10.1140/epjds/s13688-021-00268-9
Mogale HEsiefarienrhe M(2021)Optimizing Recommendation Algorithms Using Self-Similarity Matrices for Music Streaming Services2021 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD)10.1109/icABCD51485.2021.9519370(1-4)Online publication date: 5-Aug-2021
https://doi.org/10.1109/icABCD51485.2021.9519370
Paul AWu ZLiu KGong S(2021)Robust multi-objective visual bayesian personalized ranking for multimedia recommendationApplied Intelligence10.1007/s10489-021-02355-wOnline publication date: 7-Jul-2021
https://doi.org/10.1007/s10489-021-02355-w
Di Noia TMalitesta DMerra F(2020)TAaMR: Targeted Adversarial Attack against Multimedia Recommender Systems2020 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)10.1109/DSN-W50199.2020.00011(1-8)Online publication date: Jun-2020
https://doi.org/10.1109/DSN-W50199.2020.00011
Yang QZhan LHan LZhang JBi Z(2019)Recommending More Suitable Music Based on Users’ Real ContextMethionine Dependence of Cancer and Aging10.1007/978-3-030-12981-1_8(124-137)Online publication date: 7-Feb-2019
https://doi.org/10.1007/978-3-030-12981-1_8
Yang HJeong YChoi MLee J(2018)MMCFProceedings of the ACM Recommender Systems Challenge 201810.1145/3267471.3267482(1-6)Online publication date: 2-Oct-2018
https://dl.acm.org/doi/10.1145/3267471.3267482
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
Kunhui Lin Zhentuan Xu Jie Liu Qingfeng Wu Yating Chen (2016)Personalized music recommendation algorithm based on tag information2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS)10.1109/ICSESS.2016.7883055(229-232)Online publication date: Aug-2016
https://doi.org/10.1109/ICSESS.2016.7883055
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