TY - GEN
T1 - A hybrid music recommendation system by M-LSA
AU - Hu, Bin
AU - Guo, Meng
AU - Zhang, Hongbin
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Collaborative filtering
KW - Hybrid system
KW - M-LSA
KW - Music recommendation
KW - Text representation
UR - https://www.scopus.com/pages/publications/70350550160
U2 - 10.1109/CINC.2009.74
DO - 10.1109/CINC.2009.74
M3 - Conference contribution
AN - SCOPUS:70350550160
SN - 9780769536453
T3 - Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing, CINC 2009
SP - 129
EP - 132
BT - Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing, CINC 2009
T2 - 2009 International Conference on Computational Intelligence and Natural Computing, CINC 2009
Y2 - 6 June 2009 through 7 June 2009
ER -