论文标题
DVE：具有推荐系统中应用的动态变分嵌入
DVE: Dynamic Variational Embeddings with Applications in Recommender Systems
论文作者
论文摘要
嵌入是一种有用的技术，可以将高维功能投射到低维空间中，并且具有许多成功的应用程序，包括链接预测，节点分类和自然语言处理。当前的方法主要集中在静态数据上，这通常会导致涉及大型变化的应用程序的性能不令人满意。如何动态表征嵌入式特征的变化仍然很大程度上尚未探索。在本文中，我们基于经常性神经网络的最新进展，为序列感知数据引入了动态变分嵌入方法（DVE）方法。 DVE可以明确，同时同时对节点的内在性质和时间变化进行建模，这对于探索至关重要。我们进一步将DVE应用于序列感知的推荐系统，并开发端到端的神经体系结构进行链接预测。
Embedding is a useful technique to project a high-dimensional feature into a low-dimensional space, and it has many successful applications including link prediction, node classification and natural language processing. Current approaches mainly focus on static data, which usually lead to unsatisfactory performance in applications involving large changes over time. How to dynamically characterize the variation of the embedded features is still largely unexplored. In this paper, we introduce a dynamic variational embedding (DVE) approach for sequence-aware data based on recent advances in recurrent neural networks. DVE can model the node's intrinsic nature and temporal variation explicitly and simultaneously, which are crucial for exploration. We further apply DVE to sequence-aware recommender systems, and develop an end-to-end neural architecture for link prediction.
