Intelligent music playlist recommendation based on user daily behavior and music content

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5879 LNCS, Issue , 2009, Pages 671-683

  Liu, Ning Han ^a   Hsieh, Shu Ju ^a  

^a NATIONAL PINGTUNG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

Artificial neural networks; Decision tree; Music database; Music recommendation system

Indexed keywords

ARTIFICIAL NEURAL NETWORK; ARTIFICIAL NEURAL NETWORKS; DAILY ACTIVITY; MUSIC CONTENTS; MUSIC DATABASE; MUSIC RECOMMENDATION; MUSIC RECOMMENDATION SYSTEM; ON TIME; SERVICE-BASED; TIME PARAMETER; TIME STAMPS; TRAINING DATA;

DATA PROCESSING; DECISION TREES; INTELLIGENT SYSTEMS; PULSE CODE MODULATION;

NEURAL NETWORKS;

EID: 76249121508     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-10467-1_59     Document Type: Conference Paper

References (9)

1. Andric, A., Haus, G.: Automatic playlist generation based on tracking user's listening habits. Multimedia Tools and Applications 29(2) (2006)
2. Chen, H.-C., Chen, A.L.P.: A music recommendation system based on music and user grouping. Intelligent Information Systems 24(2/3), 113-132 (2005)
3. Kaji, K., Hirata, K., Nagao, K.: A music recommendation system based on annotations about listeners' preferences and situations. In: Proceedings of first international conference on automated production of cross media content for Multi-Channel Distribution, pp. 231-234 (2005)
4. Lidy, T., Rauber, A., Pertusa, A., Inesta, J.M.: Improving genre classification by combination of audio and symbolic descriptors using a transcription system. In: Proceedings of the 8th international conference on music information retrieval (2007)
5. Quinlan, J.R.: C4.5: Programs For Machine Learning. Morgan-Kaufmann, San Francisco (1993)
6. Reed, J., Lee, C.-H.: A study on attribute-based taxonomy for music information retrieval. In: Proceedings of the 8th international conference on music information retrieval (2007)
7. Schein, A., Popescul, A., Ungar, L.H., Pennock, D.M.: Generative models for cold-start recommendations. In: Proceedings of SIGIR workshop on recommender systems (2001)
8. Suyoto, I.S.H., Uitdenbogerd, A.L.: Effectiveness of note duration information for music retrieval. In: Proceedings of database systems for advanced applications (2005)
9. Yoshii, K., Goto, M., Komatani, K., Ogata, T., Okuno, H.G.: Hybrid collaborative and content-based music recommendation using probabilistic model with latent user preferences. In: Proceedings of the 7th international conference on music information retrieval (2006)