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.

我们提出了 NEUROSEQRET，它可以学习检索和排名相关连续时间事件序列的集合，以解决连续时间事件序列的检索问题，并使用 MTPP 引导神经相关模型，以及使用二元序列嵌入的优化框架来进行哈希，从而获得显着的速度优化。