Scalable Optimization for Embedding Highly-Dynamic and Recency-Sensitive Data
Xumin Chen (Tsinghua University); Peng Cui (Tsinghua University); Shiqiang Yang (Tsinghua University)
A dataset which is highly-dynamic and recency-sensitive means new data are generated in high volumes with a fast speed and of higher priority for the subsequent applications. Embedding technique is a popular research topic in recent years which aims to represent any data into low-dimensional vector space, which is widely used in different data types and have multiple applications. Generating embeddings on such data in a high-speed way is a challenging problem to consider the high dynamics and the recency sensitiveness together with both effectiveness and efficient. Popular embedding methods are usually time-consuming. As well as the common optimization methods are limited since it may not have enough time to converge or deal with recency-sensitive sample weights. This problem is still an open problem. In this paper, we propose a novel optimization method named Diffused Stochastic Gradient Descent for such highly-dynamic and recency-sensitive data. The notion of our idea is to assign recency-sensitive weights to different samples, and select samples according to their weights in calculating gradients. And after updating the embedding of the selected sample, the related samples are also updated in a diffusion strategy. We propose a Nested Segment Tree to improve the recency-sensitive weight method and the diffusion strategy into a complexity no slower than the iteration step in practice. We also theoretically prove the convergence rate of D-SGD for independent data samples, and empirically prove the efficacy of D-SGD in large-scale real datasets.