Multi-space probabilistic sequence modeling

Shuo Chen, Jiexun Xu, T. Joachims
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引用次数: 29

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.
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多空间概率序列建模
将对象嵌入欧几里得空间的学习算法已经成为解决各种问题的首选方法,从推荐和图像搜索到播放列表预测和语言建模。概率嵌入方法为解决这些问题提供了很好的方法,但是训练和存储大型整体模型的成本很高。在本文中,我们提出了一种方法,该方法不是训练一个整体模型,而是训练多个局部嵌入的两两条件模型,特别适合于序列和共现建模。我们证明了用于训练这些多空间模型的计算和内存可以在集群的多个节点上有效地并行化。专注于音乐播放列表的序列建模,我们证明了该方法在保持高模型质量的同时大大加快了训练速度。
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