Latent Inter-User Difference Modeling for LLM Personalization

• URL: http://arxiv.org/abs/2507.20849v2
• Date: Sat, 20 Sep 2025 11:57:49 GMT
• Title: Latent Inter-User Difference Modeling for LLM Personalization
• Authors: Yilun Qiu, Tianhao Shi, Xiaoyan Zhao, Fengbin Zhu, Yang Zhang, Fuli Feng,
• Abstract summary: Difference-aware Embedding-based Personalization (DEP) is a framework that models inter-user differences in the latent space instead of relying on language prompts.<n>A sparse autoencoder then filters and compresses both user-specific and difference-aware embeddings, preserving only task-relevant features.
• Score: 42.29204853138223
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) are increasingly integrated into users' daily lives, leading to a growing demand for personalized outputs. Previous work focuses on leveraging a user's own history, overlooking inter-user differences that are crucial for effective personalization. While recent work has attempted to model such differences, the reliance on language-based prompts often hampers the effective extraction of meaningful distinctions. To address these issues, we propose Difference-aware Embedding-based Personalization (DEP), a framework that models inter-user differences in the latent space instead of relying on language prompts. DEP constructs soft prompts by contrasting a user's embedding with those of peers who engaged with similar content, highlighting relative behavioral signals. A sparse autoencoder then filters and compresses both user-specific and difference-aware embeddings, preserving only task-relevant features before injecting them into a frozen LLM. Experiments on personalized review generation show that DEP consistently outperforms baseline methods across multiple metrics. Our code is available at https://github.com/SnowCharmQ/DEP.

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