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MALL: a life log based music recommendation system and portable music player

Published: 24 March 2014 Publication History

Abstract

We may like to listen to particular types of tunes under the particular situation or environment, such as events, weather, time, and place. However, it is not always easy to manually choose such particular types of tunes just by looking at metadata such as titles or artist names. It is effective and enjoyable if such tunes are automatically recommended after learning the tendency of the users. This paper presents MALL (Music Adviser with Life Log), a life log based music recommendation system and portable music player. The system records the history of listened tunes with the situation and environment on the Android-based portable music player. It then discovers association rules between the situation or environments and musical feature values of the tunes. Finally, the system recommends particular types of tunes based on the discovered association rules. We names this system MALL because it advises the tunes based on the life logs of the users.

References

[1]
R. Agrawal, R. Srikant, Fast Algorithms for Mining Association Rules, 39th International Conference on Very Large Data Bases (VLDB), 487--499, 1994.
[2]
M. Anderson, M. Ball, H. Boley, S. Greene, N. Howse, D. Lemire, S. McGrath, RACOFI: A Rule-Applying Collaborative Filtering System, Workshop on Collaboration Agents: Autonomous Agents for Collaborative Environments, 2003.
[3]
L. Baltrunas, M. Kaminskas, B. Ludwig, O. Moling. F. Ricci, A. Aydin, K.-H. Luke, R. Schwaiger, InCarMusic: Context-Aware Music Recommendations in a Car, E-Commerce and Web Technologies, LNBIP 85, 89--100, 2011.
[4]
D. Elliott, F. Hopfgartner, T. Leelanupab, Y. Moshfeghi, J. M. Jose, An Architecture for Life-long User Modelling, Lifelong User Modelling Workshop in conjunction with User Modeling, Adaptation and Personalisation (UMAP), 22--26, 2009.
[5]
T. Fukuda, Y. Morimoto, S. Morishita, T. Tokuyama, Mining Optimized Association Rules for Numeric Attributes, ACM Symposium on Principles of Database Systems (PODS '96), 182--191, 1996.
[6]
K. Kaji, K. Hirata, K. Nagao, A Music Recommendation System Based on Annotations about Listeners' Preferences and Situations, The First International Conference on Automated Production of Cross Media Content for Multi-Channel Distribution. 2005.
[7]
J.-S. Lee, J.-C. Lee, Context Awareness by Case-based Reasoning in a Music Recommendation System, Ubiquitous Computing System, LNCS 4836, 45--58, 2007.
[8]
W. Machida, T. Itoh, Lyricon: A Visual Music Selection Interface Featuring Multiple Icons, 15th International Conference on Information Visualisation (IV2011), 145--150, 2011.
[9]
J. Motoyama, S. Urazawa, T. Nakano N. Inuzuka, A Mining Algorithm Using Property Items Extracted from Sampled Examples, Inductive Logic Programming (ILP), 335--350, 2007.
[10]
Y. Nakamura, T. Itou. H. Tezuka, T Ishihara, M. Abe, Personalized TV-Program Recommendations based on Life Log, International Conference on Consumer Electronics, 143--144, 2010.
[11]
H.-S. Park, J.-O. Yoo, S.-B. Cho, A Context-Aware Music Recommendation System Using Fuzzy Bayesian Networks with Utility Theory, Fuzzy Systems and Knowledge Discovery, LNCS 4223, 970--979, 2006.
[12]
S. Pauws B. Eggen, Realization and User Evaluation of an Automatic Playlist Generator, Journal of New Music Research, 32(2), 179--192(14), 2003.
[13]
Y. Saito, T. Itoh, MusiCube: A Visual Music Recommendation System featuring Interactive Evolutionary Computing, Visual Information Communication - Information Symposium (VINCIT1), 2011.
[14]
U. Shardanand, P. Maes, Social Information Filtering: Algorithms for Automating "Word of Mouth", ACM SIGCHI Conference on Human Factors in Computing Systems, 210--217, 1995.
[15]
R. Srikant, R. Agrawal, Mining Quantitative Association Rules in Large Relational Tables, ACM SIGMOD International Conference on Management of Data, 1--12, 1996.
[16]
J.-H. Su, H.-H. Yeh, P. S. Yu, V. S. Tseng, Music Recommendation Using Content and Context Information Mining, IEEE Intelligent Systems, 25(1), 16--26, 2010.
[17]
H. Yin et al. (Eds.): Extracting Meaningful Contexts from Mobile Life Log, IDEAL 2007, LNCS 4881, 750--759, 2007.
[18]
O. Lartillot, "MIRtoolbox", available from http://www.jyu.fi/hum/laitokset/musiikki/en/research/coe/materials/mirtoolbox
[19]
RWC Music Database, http://staff.aist.go.jp/rn.goto/RWC-MDB/

Cited By

View all
  • (2021)Know Yourself: Physical and Psychological Self-Awareness With LifelogFrontiers in Digital Health10.3389/fdgth.2021.6768243Online publication date: 11-Aug-2021
  • (2014)Towards Activity Recommendation from LifelogsProceedings of the 16th International Conference on Information Integration and Web-based Applications & Services10.1145/2684200.2684298(87-96)Online publication date: 4-Dec-2014

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cover image ACM Conferences
SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing
March 2014
1890 pages
ISBN:9781450324694
DOI:10.1145/2554850
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: 24 March 2014

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Author Tags

  1. association rules
  2. life log
  3. music recommendation
  4. musical features

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  • Research-article

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SAC 2014
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SAC 2014: Symposium on Applied Computing
March 24 - 28, 2014
Gyeongju, Republic of Korea

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SAC '14 Paper Acceptance Rate 218 of 939 submissions, 23%;
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

View all
  • (2021)Know Yourself: Physical and Psychological Self-Awareness With LifelogFrontiers in Digital Health10.3389/fdgth.2021.6768243Online publication date: 11-Aug-2021
  • (2014)Towards Activity Recommendation from LifelogsProceedings of the 16th International Conference on Information Integration and Web-based Applications & Services10.1145/2684200.2684298(87-96)Online publication date: 4-Dec-2014

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