Retrieving Continuous Time Event Sequences using Neural Temporal Point Processes with Learnable Hashing

TL;DR

This paper introduces NeuroSeqRet, a novel neural framework for large-scale retrieval of continuous time event sequences, leveraging learnable hashing and MTPP-guided relevance models to improve accuracy and efficiency.

Contribution

NeuroSeqRet is the first end-to-end retrieval framework for CTES that incorporates a trainable unwarping function and MTPP-guided relevance models with learnable hashing.

Findings

NeuroSeqRet significantly improves retrieval accuracy on CTES datasets.

The proposed hashing mechanism enhances retrieval efficiency.

Multiple relevance model variants balance accuracy and computational cost.

Abstract

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.