Class-based n-gram models of natural language
Computational Linguistics
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
A music recommendation system based on music data grouping and user interests
Proceedings of the tenth international conference on Information and knowledge management
Personalization of user profiles for content-based music retrieval based on relevance feedback
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
MusicSense: contextual music recommendation using emotional allocation modeling
Proceedings of the 15th international conference on Multimedia
Towards ensemble learning for hybrid music recommendation
Proceedings of the 2007 ACM conference on Recommender systems
Context-aware query suggestion by mining click-through and session data
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A recommendation method considering users' time series contexts
Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
Factorizing personalized Markov chains for next-basket recommendation
Proceedings of the 19th international conference on World wide web
Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy
Proceedings of the fifth ACM conference on Recommender systems
Enhancing Collaborative Filtering by User Interest Expansion via Personalized Ranking
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Playlist prediction via metric embedding
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
| Hi-index | 0.00 |
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.