2013 MultiSpaceProbabilisticSequence

• (Chen et al., 2013) ⇒ Shuo Chen, Jiexun Xu, and Thorsten Joachims. (2013). “Multi-space Probabilistic Sequence Modeling.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487632

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Multi-space+Probabilistic+Sequence+Modeling
• http://dl.acm.org/citation.cfm?id=2487575.2487632&preflayout=flat#citedby

Quotes

Author Keywords

• Embedding; learning; models; music playlists; parallel computing; recommendation; sequences

Abstract

Learning algorithms that embed objects into Euclidean space have become the methods of choice for a wide range of problems, ranging from recommendation and image search to playlist prediction and language modeling. Probabilistic embedding methods provide elegant approaches to these problems, but can be expensive to train and store as a large monolithic model. In this paper, we propose a method that trains not one monolithic model, but multiple local embeddings for a class of pairwise conditional models especially suited for sequence and co-occurrence modeling. We show that computation and memory for training these multi-space models can be efficiently parallelized over many nodes of a cluster. Focusing on sequence modeling for music playlists, we show that the method substantially speeds up training while maintaining high model quality.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 MultiSpaceProbabilisticSequence	Thorsten Joachims Shuo Chen Jiexun Xu			Multi-space Probabilistic Sequence Modeling				10.1145/2487575.2487632		2013