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arXiv · 2502.10660

User Profile with Large Language Models: Construction, Updating, and Benchmarking

Nusrat Jahan Prottasha·Md Kowsher·Hafijur Raman·Israt Jahan Anny·Prakash Bhat·Ivan Garibay·Ozlem Garibay
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Abstract

User profile modeling plays a key role in personalized systems, as it requires building accurate profiles and updating them with new information. In this paper, we present two high-quality open-source user profile datasets: one for profile construction and another for profile updating. These datasets offer a strong basis for evaluating user profile modeling techniques in dynamic settings. We also show a methodology that uses large language models (LLMs) to tackle both profile construction and updating. Our method uses a probabilistic framework to predict user profiles from input text, allowing for precise and context-aware profile generation. Our experiments demonstrate that models like Mistral-7b and Llama2-7b perform strongly in both tasks. LLMs improve the precision and recall of the generated profiles, and high evaluation scores confirm the effectiveness of our approach.

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Nusrat Jahan Prottasha, Md Kowsher, Hafijur Raman, Israt Jahan Anny, Prakash Bhat, Ivan Garibay, Ozlem Garibay. 2025-02-15. User Profile with Large Language Models: Construction, Updating, and Benchmarking. https://arxiv.org/abs/2502.10660

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