Music recommendation using text analysis on song requests to radio stations

• We model a music recommendation system purely based on text analysis.
• We compare two text analysis algorithms using various data sets.
• There is close correlation between document similarity and song similarity.
• People sharing similar situational information prefer similar music.

Recommending appropriate music to users has always been a difficult task. In this paper, we propose a novel method in recommending music by analyzing the textual input of users. To this end, we mine a large corpus of documents from a Korean radio station’s online bulletin board. Each document, written by the listener, is composed of a song request associated with a brief, personal story. We assume that such stories are closely related with the background of the song requests and thus, our system performs text analysis to recommend songs that were requested from other similar stories. We evaluate our system using conventional metrics along with a user evaluation test. Results show that there is close correlation between document similarity and song similarity, indicating the potential of using text as a source to recommending music.