A Hybrid Music Recommendation System by M-LSA

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.