2012 PlaylistPredictionviaMetricEmbe

• (Chen et al., 2012) ⇒ Shuo Chen, Josh L. Moore, Douglas Turnbull, and Thorsten Joachims. (2012). “Playlist Prediction via Metric Embedding.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339643

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Cited By

• http://scholar.google.com/scholar?q=%222012%22+Playlist+Prediction+via+Metric+Embedding
• http://dl.acm.org/citation.cfm?id=2339530.2339643&preflayout=flat#citedby

Quotes

Author Keywords

• Learning; models; music playlists; recommendation; sequences; user modeling

Abstract

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 Euclidean 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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 PlaylistPredictionviaMetricEmbe	Thorsten Joachims Shuo Chen Josh L. Moore Douglas Turnbull			Playlist Prediction via Metric Embedding				10.1145/2339530.2339643		2012