Learning Temporal Point Processes for Efficient Retrieval of Continuous Time Event Sequences

TL;DR

This paper introduces NEUROSEQRET, a novel method for retrieving and ranking continuous-time event sequences using learned unwarping functions, neural relevance models, and binary embeddings for fast, accurate retrieval.

Contribution

It proposes a new retrieval framework for continuous-time event sequences that combines unwarping, neural relevance modeling, and hashing for efficiency.

Findings

Significant accuracy improvements over baseline methods.

Effective binary embeddings enable fast retrieval.

The approach balances accuracy and efficiency through model variants.

Abstract

Recent developments in predictive modeling using marked temporal point processes (MTPP) have enabled an accurate characterization of several real-world applications involving continuous-time event sequences (CTESs). However, the retrieval problem of such sequences remains largely unaddressed in literature. To tackle this, we propose NEUROSEQRET which learns to retrieve and rank a relevant set of continuous-time event sequences for a given query sequence, from a large corpus of sequences. More specifically, NEUROSEQRET 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 two variants of the relevance model which offer a tradeoff 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 leading to a significant speedup in returning top-K results for a given query sequence. Our experiments with several datasets show the significant accuracy boost of NEUROSEQRET beyond several baselines, as well as the efficacy of our hashing mechanism.