Speech and Language Processing (2nd Edition)
Speech and Language Processing (2nd Edition)
Predicting diverse subsets using structural SVMs
Proceedings of the 25th international conference on Machine learning
Exploring automatic music annotation with "acoustically-objective" tags
Proceedings of the international conference on Multimedia information retrieval
Factorizing personalized Markov chains for next-basket recommendation
Proceedings of the 19th international conference on World wide web
Statistical models of music-listening sessions in social media
Proceedings of the 19th international conference on World wide web
Collaborative topic modeling for recommending scientific articles
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Build your own music recommender by modeling internet radio streams
Proceedings of the 21st international conference on World Wide Web
A spectral algorithm for learning Hidden Markov Models
Journal of Computer and System Sciences
Recommendation challenges in web media settings
Proceedings of the sixth ACM conference on Recommender systems
Multi-space probabilistic sequence modeling
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Query-driven context aware recommendation
Proceedings of the 7th ACM conference on Recommender systems
Personalized next-song recommendation in online karaokes
Proceedings of the 7th ACM conference on Recommender systems
A survey of music similarity and recommendation from music context data
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Hi-index | 0.00 |
Digital storage of personal music collections and cloud-based music services (e.g. Pandora, Spotify) have fundamentally changed how music is consumed. In particular, automatically generated playlists have become an important mode of accessing large music collections. The key goal of automated playlist generation is to provide the user with a coherent listening experience. In this paper, we present Latent Markov Embedding (LME), a machine learning algorithm for generating such playlists. In analogy to matrix factorization methods for collaborative filtering, the algorithm does not require songs to be described by features a priori, but it learns a representation from example playlists. We formulate this problem as a regularized maximum-likelihood embedding of Markov chains in Euclidian space, and show how the resulting optimization problem can be solved efficiently. An empirical evaluation shows that the LME is substantially more accurate than adaptations of smoothed n-gram models commonly used in natural language processing.