short-paper

Hybrid retrieval approaches to geospatial music recommendation

Authors:

Dominik SchnitzerAuthors Info & Claims

SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval

Pages 793 - 796

https://doi.org/10.1145/2484028.2484146

Published: 28 July 2013 Publication History

Abstract

Recent advances in music retrieval and recommendation algorithms highlight the necessity to follow multimodal approaches in order to transcend limits imposed by methods that solely use audio, web, or collaborative filtering data. In this paper, we propose hybrid music recommendation algorithms that combine information on the music content, the music context, and the user context, in particular, integrating location-aware weighting of similarities. Using state-of-the-art techniques to extract audio features and contextual web features, and a novel standardized data set of music listening activities inferred from microblogs (MusicMicro), we propose several multimodal retrieval functions.

The main contributions of this paper are (i) a systematic evaluation of mixture coefficients between state-of-the-art audio features and web features, using the first standardized microblog data set of music listening events for retrieval purposes and (ii) novel geospatial music recommendation approaches using location information of microblog users, and a comprehensive evaluation thereof.

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Cited By

Takama YQian LShibata H(2023)Proposal of Timestamp-Based Dynamic Context Features for Music RecommendationAdvanced Computational Intelligence and Intelligent Informatics10.1007/978-981-99-7590-7_18(215-225)Online publication date: 30-Oct-2023
https://doi.org/10.1007/978-981-99-7590-7_18
Hassan MZafar NAli HYaqoob IAlaliyat SHameed I(2022)Collaborative Filtering Based Hybrid Music Recommendation SystemProceedings of International Conference on Information Technology and Applications10.1007/978-981-16-7618-5_21(239-249)Online publication date: 21-Apr-2022
https://doi.org/10.1007/978-981-16-7618-5_21
Takama YZhang JShibata H(2021)Context-aware Music Recommender System Based on Implicit FeedbackTransactions of the Japanese Society for Artificial Intelligence10.1527/tjsai.36-1_WI2-D36:1(WI2-D_1-10)Online publication date: 1-Jan-2021
https://doi.org/10.1527/tjsai.36-1_WI2-D
Show More Cited By

Index Terms

Hybrid retrieval approaches to geospatial music recommendation
1. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval

July 2013

1188 pages

ISBN:9781450320344

DOI:10.1145/2484028

General Chairs:
Gareth J.F. Jones
Dublin City University, Ireland
,
Páraic Sheridan
Dublin City University, Ireland
,
Program Chairs:
Diane Kelly
University of North Carolina, Chapel Hill, USA
,
Maarten de Rijke
University of Amsterdam, The Netherlands
,
Tetsuya Sakai
Microsoft Research Asia, China

Copyright © 2013 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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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 July 2013

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SIGIR '13

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SIGIR

SIGIR '13: The 36th International ACM SIGIR conference on research and development in Information Retrieval

July 28 - August 1, 2013

Dublin, Ireland

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SIGIR '13 Paper Acceptance Rate 73 of 366 submissions, 20%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Takama YQian LShibata H(2023)Proposal of Timestamp-Based Dynamic Context Features for Music RecommendationAdvanced Computational Intelligence and Intelligent Informatics10.1007/978-981-99-7590-7_18(215-225)Online publication date: 30-Oct-2023
https://doi.org/10.1007/978-981-99-7590-7_18
Hassan MZafar NAli HYaqoob IAlaliyat SHameed I(2022)Collaborative Filtering Based Hybrid Music Recommendation SystemProceedings of International Conference on Information Technology and Applications10.1007/978-981-16-7618-5_21(239-249)Online publication date: 21-Apr-2022
https://doi.org/10.1007/978-981-16-7618-5_21
Takama YZhang JShibata H(2021)Context-aware Music Recommender System Based on Implicit FeedbackTransactions of the Japanese Society for Artificial Intelligence10.1527/tjsai.36-1_WI2-D36:1(WI2-D_1-10)Online publication date: 1-Jan-2021
https://doi.org/10.1527/tjsai.36-1_WI2-D
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
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
Ma RHu XZhang QHuang XJiang YPiwowarski BChevalier MGaussier EMaarek YNie JScholer F(2019)Hot Topic-Aware Retweet Prediction with Masked Self-attentive ModelProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331236(525-534)Online publication date: 18-Jul-2019
https://dl.acm.org/doi/10.1145/3331184.3331236
Ferraro ABogers TSaid ABrusilovsky PTikk D(2019)Music cold-start and long-tail recommendationProceedings of the 13th ACM Conference on Recommender Systems10.1145/3298689.3347052(586-590)Online publication date: 10-Sep-2019
https://dl.acm.org/doi/10.1145/3298689.3347052
Ma RQiu XZhang QHu XJiang YHuang X(2019)Co-attention Memory Network for Multimodal Microblog's Hashtag RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.2932406(1-1)Online publication date: 2019
https://doi.org/10.1109/TKDE.2019.2932406
Shao XCheng ZKankanhalli M(2019)Music auto-tagging based on the unified latent semantic modelingMultimedia Tools and Applications10.1007/s11042-018-5632-278:1(161-176)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1007/s11042-018-5632-2
Liu MHu XSchedl M(2018)The relation of culture, socio-economics, and friendship to music preferences: A large-scale, cross-country studyPLOS ONE10.1371/journal.pone.020818613:12(e0208186)Online publication date: 14-Dec-2018
https://doi.org/10.1371/journal.pone.0208186
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