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.

中文翻译：

---

DVE：动态变分嵌入在推荐系统中的应用

嵌入是一种将高维特征投影到低维空间的有用技术，它有许多成功的应用，包括链接预测、节点分类和自然语言处理。当前的方法主要关注静态数据，这通常会导致随着时间的推移发生较大变化的应用程序的性能不令人满意。如何动态表征嵌入特征的变化在很大程度上仍未得到探索。在本文中，我们基于循环神经网络的最新进展介绍了一种用于序列感知数据的动态变分嵌入 (DVE) 方法。DVE 可以同时显式地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 应用于序列感知推荐系统，