research-article

Improving Context-Aware Music Recommender Systems: Beyond the Pre-filtering Approach

Authors:

Günther SpechtAuthors Info & Claims

ICMR '17: Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval

Pages 201 - 208

https://doi.org/10.1145/3078971.3078980

Published: 06 June 2017 Publication History

Abstract

Over the last years, music consumption has changed fundamentally: people switch from private, mostly limited music collections to huge public music collections provided by music streaming platforms. Thus, the amount of available music has increased dramatically and music streaming platforms heavily rely on recommender systems to assist users in discovering music they like. Incorporating the context of users has been shown to improve the quality of recommendations. Previous approaches based on pre-filtering suffered from a split dataset. In this work, we present a context-aware recommender system based on factorization machines that extracts information about the user's context from the names of the user's playlists. Based on a dataset comprising 15,000 users and 1.8 million tracks we show that our proposed approach outperforms the pre-filtering approach substantially in terms of accuracy of the computed recommendations.

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Cited By

Nguyen Hoai Nam L(2022)Incorporating textual reviews in the learning of latent factors for recommender systemsElectronic Commerce Research and Applications10.1016/j.elerap.2022.10113352:COnline publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1016/j.elerap.2022.101133
Lozano Murciego ÁJiménez-Bravo DValera Román ADe Paz Santana JMoreno-García M(2021)Context-Aware Recommender Systems in the Music Domain: A Systematic Literature ReviewElectronics10.3390/electronics1013155510:13(1555)Online publication date: 27-Jun-2021
https://doi.org/10.3390/electronics10131555
Li HChen YZhang DWu L(2021)An Intelligent Recommendation System for Performance Equipment Operation and Maintenance via Deep Neural Network and Attention Mechanism2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)10.1109/DDCLS52934.2021.9455655(1464-1468)Online publication date: 14-May-2021
https://doi.org/10.1109/DDCLS52934.2021.9455655
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Index Terms

Improving Context-Aware Music Recommender Systems: Beyond the Pre-filtering Approach
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Collaborative filtering
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
    2. Specialized information retrieval
      1. Multimedia and multimodal retrieval
        Music retrieval

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Information & Contributors

Information

Published In

cover image ACM Conferences

ICMR '17: Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval

June 2017

524 pages

ISBN:9781450347013

DOI:10.1145/3078971

General Chairs:
Bogdan Ionescu
University Politehnica of Bucharest, Romania
,
Nicu Sebe
University of Trento, Italy
,
Program Chairs:
Jiashi Feng
National University of Singapore, Singapore
,
Martha Larson
Radboud University & Delft University of Technology, The Netherlands
,
Rainer Lienhart
University of Augsburg, Germany
,
Cees Snoek
University of Amsterdam & Qualcomm Research Netherlands, The Netherlands

Copyright © 2017 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 the author(s) 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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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

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Published: 06 June 2017

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ICMR '17

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SIGMM

ICMR '17: International Conference on Multimedia Retrieval

June 6 - 9, 2017

Bucharest, Romania

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ICMR '17 Paper Acceptance Rate 33 of 95 submissions, 35%;

Overall Acceptance Rate 254 of 830 submissions, 31%

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Cited By

Nguyen Hoai Nam L(2022)Incorporating textual reviews in the learning of latent factors for recommender systemsElectronic Commerce Research and Applications10.1016/j.elerap.2022.10113352:COnline publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1016/j.elerap.2022.101133
Lozano Murciego ÁJiménez-Bravo DValera Román ADe Paz Santana JMoreno-García M(2021)Context-Aware Recommender Systems in the Music Domain: A Systematic Literature ReviewElectronics10.3390/electronics1013155510:13(1555)Online publication date: 27-Jun-2021
https://doi.org/10.3390/electronics10131555
Li HChen YZhang DWu L(2021)An Intelligent Recommendation System for Performance Equipment Operation and Maintenance via Deep Neural Network and Attention Mechanism2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)10.1109/DDCLS52934.2021.9455655(1464-1468)Online publication date: 14-May-2021
https://doi.org/10.1109/DDCLS52934.2021.9455655
Yürekli ABilge AKaleli C(2021)Exploring playlist titles for cold-start music recommendation: an effectiveness analysisJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-020-02777-312:11(10125-10144)Online publication date: 3-Jan-2021
https://doi.org/10.1007/s12652-020-02777-3
Yousefian Jazi SKaedi MFatemi A(2021)An emotion-aware music recommender system: bridging the user’s interaction and music recommendationMultimedia Tools and Applications10.1007/s11042-020-10386-780:9(13559-13574)Online publication date: 1-Apr-2021
https://dl.acm.org/doi/10.1007/s11042-020-10386-7
Pichl MZangerle E(2021)User models for multi-context-aware music recommendationMultimedia Tools and Applications10.1007/s11042-020-09890-780:15(22509-22531)Online publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1007/s11042-020-09890-7
Liu DLi JDu BChang JGao RWu Y(2021)A hybrid neural network approach to combine textual information and rating information for item recommendationKnowledge and Information Systems10.1007/s10115-020-01528-263:3(621-646)Online publication date: 1-Mar-2021
https://dl.acm.org/doi/10.1007/s10115-020-01528-2
Han YYamanishi RNishihara Y(2020)Music Retrieval Focusing on Lyrics with Summary of Tourist-spot Reviews Based on Shared Word-vectors2020 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)10.1109/TAAI51410.2020.00022(73-78)Online publication date: Dec-2020
https://doi.org/10.1109/TAAI51410.2020.00022
Zhang JTakama Y(2020)Proposal of Context-Aware Music Recommender System Using Negative SamplingAdvances in Artificial Intelligence10.1007/978-3-030-39878-1_11(114-125)Online publication date: 4-Feb-2020
https://doi.org/10.1007/978-3-030-39878-1_11
Tran TSweeney RLee KPiwowarski BChevalier MGaussier EMaarek YNie JScholer F(2019)Adversarial Mahalanobis Distance-based Attentive Song Recommender for Automatic Playlist ContinuationProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331234(245-254)Online publication date: 18-Jul-2019
https://dl.acm.org/doi/10.1145/3331184.3331234
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