Abstract: For recommending songs to a user, one effective approach is to represent artists and songs with latent vectors and predict the user's preference toward the songs. Although the latent vectors represent the characteristics of artists and songs well, they have typically been used only for computing the preference score. In this paper, we discuss how we can leverage these vectors for realizing applications that enable users to search for songs from new perspectives. To this end, by embedding song/artist vectors into the same feature space, we first propose two concepts of artist-song relationships: overall similarity and prominent affinity. Overall similarity is the degree to which the characteristics of a song are similar overall to the characteristics of the artist; while prominent affinity is the degree to which a song prominently represents the characteristics of the artist. By using Last.fm play logs for two years, we analyze the characteristics of the concepts. Moreover, based on the analysis results, we propose three applications for song search. Through case studies, we demonstrate that our proposed applications are beneficial for searching for songs according to the users' various search intents.