LDA-based music recommendation with CF-based similar user selection

This paper presents a Latent Dirichlet Allocation (LDA)-based music recommendation method with collaborative filtering (CF)-based similar user selection. By applying LDA to music, we can estimate latent topics of music. However, we have to effectively reduce the size of the target dataset applied to LDA in order to recommend music from a large dataset. Hence, we use CF techniques, which recommend items using evaluation information of users who have similar tastes to a target user. Therefore, the proposed method limits the size of the dataset by using information of similar users and enables the recommendation of music considering latent topics of music. By using the idea of CF, our method can use LDA for music recommendation. Experimental results show the effectiveness of our method.