A Hybrid Music Recommendation System by M-LSA

Author

Hu, Bin ; Guo, Meng ; Zhang, Hongbin

Author_Institution

Coll. of Comput. Sci., Beijing Univ. of Technol., Beijing, China

Volume

1

fYear

2009

fDate

6-7 June 2009

Firstpage

129

Lastpage

132

Abstract

In this paper, a hybrid music recommendation system is proposed, which combines collaborative filtering and content-base recommendation. Neither of these two parts can make full use of all the information. Our method integrates both user rating and music content information using an expansion method of LSA (latent semantic analysis) called M-LSA. We use a text representation for music content information, which is obtained by K-means clustering or HMM method. Experiments on the data of 300 popular songs show that the proposed approach achieves satisfactory results.

Keywords

hidden Markov models; information filtering; music; natural language processing; pattern clustering; text analysis; K-means clustering; collaborative filtering; content-base recommendation; hidden Markov model; hybrid music recommendation system; latent semantic analysis; text representation; Collaborative work; Computer science; Educational institutions; Filtering; Matrix decomposition; Mel frequency cepstral coefficient; Multimedia databases; Multiple signal classification; Recommender systems; Space technology; M-LSA; collaborative filtering; hybrid system; music recommendation; text representation;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Natural Computing, 2009. CINC '09. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-0-7695-3645-3

Type

conf

DOI

10.1109/CINC.2009.74

Filename

5231180