arXiv subjects

Mikhail Orlov

Publications and source records attributed to Mikhail Orlov.

3 recordsLinked to original sources

arXiv2025

LLM4ES: Learning User Embeddings from Event Sequences via Large Language Models

This paper presents LLM4ES, a novel framework that exploits large pre-trained language models (LLMs) to derive user embeddings from event sequences. Event sequences are transformed into a textual representation, which is subsequently used to fine-tune an LLM through next-token prediction to generate high-quality embeddings. We introduce a text enrichment technique that enhances LLM adaptation to event sequence data, improving representation quality for low-variability domains. Experimental results demonstrate that LLM4ES achieves state-of-the-art performance in user classification tasks in financial and other domains, outperforming existing embedding methods. The resulting user embeddings can be incorporated into a wide range of applications, from user segmentation in finance to patient outcome prediction in healthcare.

cs.IR↗

arXiv2024

ESQA: Event Sequences Question Answering

Event sequences (ESs) arise in many practical domains including finance, retail, social networks, and healthcare. In the context of machine learning, event sequences can be seen as a special type of tabular data with annotated timestamps. Despite the importance of ESs modeling and analysis, little effort was made in adapting large language models (LLMs) to the ESs domain. In this paper, we highlight the common difficulties of ESs processing and propose a novel solution capable of solving multiple downstream tasks with little or no finetuning. In particular, we solve the problem of working with long sequences and improve time and numeric features processing. The resulting method, called ESQA, effectively utilizes the power of LLMs and, according to extensive experiments, achieves state-of-the-art results in the ESs domain.

cs.CL↗

arXiv2023

Continuous-time convolutions model of event sequences

Event sequences often emerge in data mining. Modeling these sequences presents two main challenges: methodological and computational. Methodologically, event sequences are non-uniform and sparse, making traditional models unsuitable. Computationally, the vast amount of data and the significant length of each sequence necessitate complex and efficient models. Existing solutions, such as recurrent and transformer neural networks, rely on parametric intensity functions defined at each moment. These functions are either limited in their ability to represent complex event sequences or notably inefficient. We propose COTIC, a method based on an efficient convolution neural network designed to handle the non-uniform occurrence of events over time. Our paper introduces a continuous convolution layer, allowing a model to capture complex dependencies, including, e.g., the self-excitement effect, with little computational expense. COTIC outperforms existing models in predicting the next event time and type, achieving an average rank of 1.5 compared to 3.714 for the nearest competitor. Furthermore, COTIC`s ability to produce effective embeddings demonstrates its potential for various downstream tasks. Our code is open and available at: https://github.com/VladislavZh/COTIC.

cs.LG↗