Paper Info
Title
Personalized next-song recommendation in online karaokes
Abstract
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.
Year
DOI
Venue
2013
10.1145/2507157.2507215
RecSys
Keywords
Field
DocType
euclidean space,personalized next-song recommendation,markov embedding,next-song recommendation strategy,last song,experimental evaluation,online karaokes,content information,candidate song,online karaoke user,personalized recommendation,embed song,personalization
Embedding,Ranking,Computer science,Markov chain,Euclidean space,Singing,Multimedia,Personalization
Conference
Citations 
PageRank 
References 
26
1.07
13
Authors
7
Name
Order
Citations
PageRank
Xiang Wu1682.23
Liu Qi21027106.48
Enhong Chen32106165.57
Liang He4332.57
Jingsong Lv5393.61
Can Cao6271.76
Guoping Hu730937.32