使用带有可学习哈希的神经时间点过程检索连续时间事件序列
Retrieving Continuous Time Event Sequences using Neural Temporal Point Processes with Learnable Hashing
AI总结：
针对连续时间事件序列（CTES）大规模检索被忽视的问题，提出首个端到端CTES检索框架NeuroSeqRet，结合可训练反扭曲、MTPP引导的相关性模型与可学习哈希，实现准确率显著提升。
摘要：
时间序列已在各类现实应用中无处不在。因此，过去几年中，以连续时间事件序列（CTES）形式生成的数据量呈指数级增长。当前针对CTES数据集的大量研究聚焦于设计模型以处理下游任务，例如下一事件预测、长期预测、序列分类等。基于标记时间点过程（MTPP）的预测建模领域的最新进展，已实现了对涉及CTES的多种现实应用的精准表征。然而，由于这些CTES数据集的复杂特性，大规模时间序列检索任务在以往文献中被忽视。具体而言，CTES检索指的是对于输入的查询序列，检索系统必须从大型语料库中返回按相关性排序的相关序列列表。为解决这一问题，我们提出了NeuroSeqRet，这是首个专门为端到端CTES检索设计的框架。具体来说，NeuroSeqRet在标准检索框架的基础上进行了多项改进：首先对查询序列应用可训练的反扭曲函数，使其能与语料库序列进行比较，尤其是当相关的查询-语料库对各自具有不同属性时。随后，它将经过反扭曲的查询序列和语料库序列输入由MTPP引导的神经相关性模型。我们基于准确率和效率之间的权衡，开发了四种适用于不同类型应用的相关性模型变体。我们还提出了一种优化框架，可从相关性分数中学习适用于局部敏感哈希的二值序列嵌入。我们的实验表明，NeuroSeqRet的准确率有显著提升，同时我们的哈希机制也具备有效性。
英文摘要：
Temporal sequences have become pervasive in various real-world applications. Consequently, the volume of data generated in the form of continuous time-event sequence(s) or CTES(s) has increased exponentially in the past few years. Thus, a significant fraction of the ongoing research on CTES datasets involves designing models to address downstream tasks such as next-event prediction, long-term forecasting, sequence classification etc. The recent developments in predictive modeling using marked temporal point processes (MTPP) have enabled an accurate characterization of several real-world applications involving the CTESs. However, due to the complex nature of these CTES datasets, the task of large-scale retrieval of temporal sequences has been overlooked by the past literature. In detail, by CTES retrieval we mean that for an input query sequence, a retrieval system must return a ranked list of relevant sequences from a large corpus. To tackle this, we propose NeuroSeqRet, a first-of-its-kind framework designed specifically for end-to-end CTES retrieval. Specifically, NeuroSeqRet introduces multiple enhancements over standard retrieval frameworks and first applies a trainable unwarping function on the query sequence which makes it comparable with corpus sequences, especially when a relevant query-corpus pair has individually different attributes. Next, it feeds the unwarped query sequence and the corpus sequence into MTPP-guided neural relevance models. We develop four variants of the relevance model for different kinds of applications based on the trade-off between accuracy and efficiency. We also propose an optimization framework to learn binary sequence embeddings from the relevance scores, suitable for the locality-sensitive hashing. Our experiments show the significant accuracy boost of NeuroSeqRet as well as the efficacy of our hashing mechanism.
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