LLM4ES: Learning User Embeddings from Event Sequences via Large Language Models
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918251489918976 |
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| author | Shestov, Aleksei Zoloev, Omar Makarenko, Maksim Orlov, Mikhail Fadeev, Egor Kireev, Ivan Savchenko, Andrey |
| author_facet | Shestov, Aleksei Zoloev, Omar Makarenko, Maksim Orlov, Mikhail Fadeev, Egor Kireev, Ivan Savchenko, Andrey |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05688 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM4ES: Learning User Embeddings from Event Sequences via Large Language Models Shestov, Aleksei Zoloev, Omar Makarenko, Maksim Orlov, Mikhail Fadeev, Egor Kireev, Ivan Savchenko, Andrey Information Retrieval 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. |
| title | LLM4ES: Learning User Embeddings from Event Sequences via Large Language Models |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2508.05688 |