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

• URL: http://arxiv.org/abs/2307.09613v1
• Date: Thu, 13 Jul 2023 18:54:50 GMT
• Title: Retrieving Continuous Time Event Sequences using Neural Temporal Point Processes with Learnable Hashing
• Authors: Vinayak Gupta and Srikanta Bedathur and Abir De
• Abstract summary: We propose NeuroSeqRet, a first-of-its-kind framework designed specifically for end-to-end CTES retrieval. We develop four variants of the relevance model for different kinds of applications based on the trade-off between accuracy and efficiency. Our experiments show the significant accuracy boost of NeuroSeqRet as well as the efficacy of our hashing mechanism.
• Score: 24.963828650935913
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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