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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.11016 |
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| _version_ | 1866912644311547904 |
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| author | Gao, Ziyi Xu, Yike Yuan, Jiahao Wang, Baokun Wen, Jinyong Lin, Xiaotong Liu, Yun Fu, Xing Cheng, Yu Liu, Yongchao Wang, Weiqiang Xie, Zhongle |
| author_facet | Gao, Ziyi Xu, Yike Yuan, Jiahao Wang, Baokun Wen, Jinyong Lin, Xiaotong Liu, Yun Fu, Xing Cheng, Yu Liu, Yongchao Wang, Weiqiang Xie, Zhongle |
| contents | User representation modeling has become increasingly crucial for personalized applications, yet existing approaches struggle with generalizability across domains and sensitivity to noisy behavioral signals. We present InstructUE, an instruction-aware user embedding foundation model that leverages large language models (LLMs) to generate general and instruction-aware user representations. InstructUE introduces a multi-encoder architecture with a lightweight adapter that efficiently processes heterogeneous data from six different sources while preserving their structural characteristics. Additionally, it proposes a novel contrastive-autoregressive training framework that bridges language and representation spaces through a curated UserQA dataset. The contrastive-autoregressive training framework simultaneously leverages autoregressive learning to capture domain knowledge in language space and contrastive learning to align user-text embeddings in representation space, thereby enhancing the instruction-awareness and noise-robustness of user embeddings. Through extensive experiments on real-world applications, we demonstrate that InstructUE significantly outperforms existing methods across multiple domains including user prediction, marketing, and recommendation scenarios. Our results show that instruction-aware user modeling can effectively achieve instruction-guided denoising of user information in specific scenarios, paving the way for more generalizable and robust user representation learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_11016 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Instruction-aware User Embedding via Synergistic Language and Representation Modeling Gao, Ziyi Xu, Yike Yuan, Jiahao Wang, Baokun Wen, Jinyong Lin, Xiaotong Liu, Yun Fu, Xing Cheng, Yu Liu, Yongchao Wang, Weiqiang Xie, Zhongle Machine Learning User representation modeling has become increasingly crucial for personalized applications, yet existing approaches struggle with generalizability across domains and sensitivity to noisy behavioral signals. We present InstructUE, an instruction-aware user embedding foundation model that leverages large language models (LLMs) to generate general and instruction-aware user representations. InstructUE introduces a multi-encoder architecture with a lightweight adapter that efficiently processes heterogeneous data from six different sources while preserving their structural characteristics. Additionally, it proposes a novel contrastive-autoregressive training framework that bridges language and representation spaces through a curated UserQA dataset. The contrastive-autoregressive training framework simultaneously leverages autoregressive learning to capture domain knowledge in language space and contrastive learning to align user-text embeddings in representation space, thereby enhancing the instruction-awareness and noise-robustness of user embeddings. Through extensive experiments on real-world applications, we demonstrate that InstructUE significantly outperforms existing methods across multiple domains including user prediction, marketing, and recommendation scenarios. Our results show that instruction-aware user modeling can effectively achieve instruction-guided denoising of user information in specific scenarios, paving the way for more generalizable and robust user representation learning. |
| title | Instruction-aware User Embedding via Synergistic Language and Representation Modeling |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.11016 |