In this paper, we propose Personalized Markov Embedding (PME), a next-song recommendation strategy for online karaoke users. By modeling the sequential singing behavior, we first embed songs and users into a Euclidean space in which distances between songs and users reflect the strength of their relationships. Then, given each user's last song, we can generate personalized recommendations by ranking the candidate songs according to the embedding. Moreover, PME can be trained without any requirement of content information. Finally, we perform an experimental evaluation on a real world data set provided by ihou.com which is an online karaoke website launched by iFLYTEK, and the results clearly demonstrate the effectiveness of PME.
REF proposed Personalized Markov Embedding (PME) to map the users and songs to an Euclidean space by modeling the sequential singing behaviours, the prediction and recommendation are conducted on the base of the embeddings.
Personalized next-song recommendation in online karaokes
RecSys '13
Computer Science