tutorial

Just-for-Me: An Adaptive Personalization System for Location-Aware Social Music Recommendation

Authors:

Jialie ShenAuthors Info & Claims

ICMR '14: Proceedings of International Conference on Multimedia Retrieval

Pages 185 - 192

https://doi.org/10.1145/2578726.2578751

Published: 01 April 2014 Publication History

Abstract

The fast growth of online communities and increasing popularity of internet-accessing smart devices have significantly changed the way people consume and share music. As an emerging technology to facilitate effective music retrieval on the move, intelligent recommendation has been recently received great attentions in recent years. While a large amount of efforts have been invested in the field, the technology is still in its infancy. One of the major reasons for this stagnation is due to inability of the existing approaches to comprehensively take multiple kinds of contextual information into account. In the paper, we present a novel recommender system called Just-for-Me to facilitate effective social music recommendation by considering users' location related contexts as well as global music popularity trends. We also develop an unified recommendation model to integrate the contextual factors as well as music contents simultaneously. Furthermore, pseudo-observations are proposed to overcome the cold-start and sparsity problems. An extensive experimental study based on different test collections demonstrates that Just-for-Me system can significantly improve the recommendation performance at various geo-locations.

References

[1]

G. Adomavicius et al. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE TKDE, 17:743--749, 2005.

Digital Library

[2]

G. Adomavicius et al. Context-aware recommender systems. In Recommender systems handbook, pages 217--253. Springer, 2011.

[3]

L. Baltrunas et al. Incarmusic: Context-aware music recommendations in a car. In EC-Web, 2011.

[4]

M. Braunhofer et al. Recommending music for places of interest in a mobile travel guide. In ACM RecSys, 2011.

Digital Library

[5]

M. Braunhofer et al. Location-aware music recommendation. IJMIR, 2(1):31--44, 2013.

[6]

R. Burke. Hybrid web recommender systems. In The adaptive web, pages 377--408. Springer, 2007.

[7]

R. Cai et al. Musicsense: Contextual music recommendation using emotional allocation modeling. In ACM MM, 2007.

Digital Library

[8]

C. Chang et al. LIBSVM: A library for support vector machines. ACM TIST, 2, 2011.

Digital Library

[9]

J. Donaldson. A hybrid social-acoustic recommendation system for popular music. In ACM RecSys, 2007.

Digital Library

[10]

S. Dornbush et al. Xpod: A human activity aware learning mobile music player. In Proc. Workshop on Ambient Intelligence, IJCAI, 2007.

Digital Library

[11]

T. Hofmann. Probabilistic latent semantic analysis. In UAI, 1999.

Digital Library

[12]

M. Kaminskas et al. Knowledge-based music retrieval for places of interest. In MIRUM, pages 19--24. ACM, 2012.

Digital Library

[13]

M. Kaminskas et al. Location-aware music recommendation using auto-tagging and hybrid matching. In ACM RecSys, 2013.

Digital Library

[14]

M. Kaminskas et al. Contextual music information retrieval and recommendation: state of the art and challenges. Computer Science Review, 6(2-3):89--119, 2012.

[15]

J. Lee et al. Context awareness by case-based reasoning in a music recommendation system. In Ubiquitous Computing Systems, pages 45--58. 2007.

Digital Library

[16]

A. Lehtiniemi. Evaluating supermusic: streaming context-aware mobile music service. In ACM ACE, 2008.

Digital Library

[17]

B. Logan et al. Mel frequency cepstral coefficients for music modeling. In ISMIR, 2000.

[18]

M. Pazzani et al. Content-based recommendation systems. In The adaptive web, pages 325--341. Springer, 2007.

[19]

A. Popescul et al. Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In UAI, 2001.

Digital Library

[20]

S. Rho et al. Svr-based music mood classification and context-based music recommendation. In ACM MM, 2009.

Digital Library

[21]

F. Ricci. Context-aware music recommender systems: workshop keynote abstract. In ACM WWW, 2012.

Digital Library

[22]

J. Schafer et al. Collaborative filtering recommender systems. In The adaptive web, pages 291--324. 2007.

[23]

M. Schedl. Analyzing the potential of microblogs for spatio-temporal popularity estimation of music artists. In Proceedings of the IJCAI. Citeseer, 2011.

[24]

M. Schedl et al. What's hot? estimating country specific artist popularity. In ISMIR, 2010.

[25]

M. Schedl et al. Harvesting microblogs for contextual music similarity estimation: a co-occurrence-based framework. Multimedia Systems, pages 1--13, 2013.

[26]

M. Schedl et al. Multimedia information retrieval: music and audio. In ACM MM, 2013.

Digital Library

[27]

M. Schedl et al. Location-aware music artist recommendation. In MMM, 2014.

Digital Library

[28]

J. Shen et al. Towards effective content-based music retrieval with multiple acoustic feature combination. IEEE TMM, 8(6):1179--1189, 2006.

Digital Library

[29]

J. Shen et al. Modeling concept dynamics for large scale music search. In ACM SIGIR, 2012.

Digital Library

[30]

J.-H. Su et al. Music recommendation using content and context information mining. IEEE Intelligent Systems, 25:16--26, 2010.

Digital Library

[31]

X. Wang et al. Context-aware mobile music recommendation for daily activities. In ACM MM, 2012.

Digital Library

[32]

K. Yoshii et al. An efficient hybrid music recommender system using an incrementally trainable probabilistic generative model. IEEE TASLP, 16(2):435--447, 2008.

Digital Library

[33]

B. Zhang et al. Compositemap: a novel music similarity measure for personalized multimodal music search. In ACM MM, 2009.

Digital Library

Cited By

Gabbolini GBridge D(2024)Surveying More Than Two Decades of Music Information Retrieval Research on PlaylistsACM Transactions on Intelligent Systems and Technology10.1145/368839815:6(1-68)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3688398
Amiri BShahverdi NHaddadi AGhahremani Y(2024)Beyond the Trends: Evolution and Future Directions in Music Recommender Systems ResearchIEEE Access10.1109/ACCESS.2024.338668412(51500-51522)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3386684
Liu LGuan YWang ZShen RZheng GFu XYu XJiang J(2024)An interactive food recommendation system using reinforcement learningExpert Systems with Applications10.1016/j.eswa.2024.124313254(124313)Online publication date: Nov-2024
https://doi.org/10.1016/j.eswa.2024.124313
Show More Cited By

Index Terms

Just-for-Me: An Adaptive Personalization System for Location-Aware Social Music Recommendation
1. Applied computing
  1. Arts and humanities
    1. Sound and music computing
2. Information systems
  1. Information retrieval
    1. Specialized information retrieval
      1. Multimedia and multimodal retrieval
        Music retrieval

Recommendations

User Similarity Adjustment for Improved Recommendations
MIKE 2015: Proceedings of the Third International Conference on Mining Intelligence and Knowledge Exploration - Volume 9468

Recommender systems are becoming more and more attractive in both research and commercial communities due to Information overload problem and the popularity of the Internet applications. Collaborative Filtering, a popular branch of recommendation ...
Using a trust network to improve top-N recommendation
RecSys '09: Proceedings of the third ACM conference on Recommender systems

Top-N item recommendation is one of the important tasks of recommenders. Collaborative filtering is the most popular approach to building recommender systems which can predict ratings for a given user and item. Collaborative filtering can be extended ...
Typicality-Based Collaborative Filtering Recommendation

Collaborative filtering (CF) is an important and popular technology for recommender systems. However, current CF methods suffer from such problems as data sparsity, recommendation inaccuracy, and big-error in predictions. In this paper, we borrow ideas ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICMR '14: Proceedings of International Conference on Multimedia Retrieval

April 2014

564 pages

ISBN:9781450327824

DOI:10.1145/2578726

Conference Chairs:
Mohan Kankanhalli
National University of Singapore
,
Stefan Rueger
The Open University, UK
,
R. Manmatha
A9.com, USA
,
General Chairs:
Joemon Jose
University of Glasgow, UK
,
Keith van Rijsbergen
University of Glasgow, UK

Copyright © 2014 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]

In-Cooperation

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 April 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Tutorial
Research
Refereed limited

Conference

ICMR '14

ICMR '14: International Conference on Multimedia Retrieval

April 1 - 4, 2014

Glasgow, United Kingdom

Acceptance Rates

ICMR '14 Paper Acceptance Rate 21 of 111 submissions, 19%;

Overall Acceptance Rate 254 of 830 submissions, 31%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

64
Total Citations
View Citations
737
Total Downloads

Downloads (Last 12 months)31
Downloads (Last 6 weeks)1

Reflects downloads up to 27 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Gabbolini GBridge D(2024)Surveying More Than Two Decades of Music Information Retrieval Research on PlaylistsACM Transactions on Intelligent Systems and Technology10.1145/368839815:6(1-68)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3688398
Amiri BShahverdi NHaddadi AGhahremani Y(2024)Beyond the Trends: Evolution and Future Directions in Music Recommender Systems ResearchIEEE Access10.1109/ACCESS.2024.338668412(51500-51522)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3386684
Liu LGuan YWang ZShen RZheng GFu XYu XJiang J(2024)An interactive food recommendation system using reinforcement learningExpert Systems with Applications10.1016/j.eswa.2024.124313254(124313)Online publication date: Nov-2024
https://doi.org/10.1016/j.eswa.2024.124313
Patel SJethva HPatel V(2024)Music Recommendation Systems: Techniques, Use Cases, and ChallengesICT: Smart Systems and Technologies10.1007/978-981-99-9489-2_25(285-296)Online publication date: 16-Mar-2024
https://doi.org/10.1007/978-981-99-9489-2_25
Tamada TSaga R(2023)Triple Supportive Information for Matrix Factorization with Image, Text, and Social NetworksHuman Interface and the Management of Information10.1007/978-3-031-35132-7_46(622-633)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.1007/978-3-031-35132-7_46
Belk R(2022)Artificial Emotions and Love and Sex Doll Service WorkersJournal of Service Research10.1177/1094670521106369225:4(521-536)Online publication date: 14-Apr-2022
https://doi.org/10.1177/10946705211063692
Hao JDun YZhao GWu YQian X(2022)Annular-Graph Attention Model for Personalized Sequential RecommendationIEEE Transactions on Multimedia10.1109/TMM.2021.309718624(3381-3391)Online publication date: 2022
https://doi.org/10.1109/TMM.2021.3097186
Hasan EDing CCuzzocrea A(2022)Multi-criteria Rating and Review based Recommendation Model2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020741(5494-5503)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020741
Chen GZhao GZhu LZhuo ZQian X(2022)Combining Non-sampling and Self-attention for Sequential RecommendationInformation Processing and Management: an International Journal10.1016/j.ipm.2021.10281459:2Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1016/j.ipm.2021.102814
Assuncao WPiccolo LZaina L(2022)Considering emotions and contextual factors in music recommendation: a systematic literature reviewMultimedia Tools and Applications10.1007/s11042-022-12110-z81:6(8367-8407)Online publication date: 2-Feb-2022
https://dl.acm.org/doi/10.1007/s11042-022-12110-z
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents