Recommending Music Based on Probabilistic Latent Semantic Analysis on Korean Radio Episodes

Recommending music that satisfies the user's taste has been a challenging problem. Previous works on music recommendation system focused on the user's purchase history or the content of the music. In this paper, we propose a music recommendation system purely based on analyzing textual input of the users. We first mine a large corpus of Korean radio episodes, which is written by the listener. Each episode is composed of a personal story and a song request which we assume to be somehow related to the story. We then performing probabilistic Latent Semantic Analysis (pLSA) to find similar documents and recommend music that are associated to those documents. We evaluate our system by computing the mean reciprocal rank and mean average precision, which are both conventional metrics in evaluating information retrieval systems. The result shows that music similarity and document similarity are closely correlated, and thus it is possible to recommend music purely based on text analysis.