Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations
Recently, large language models (LLMs) have advanced recommendation systems (RSs), and recent works have begun to explore how to integrate LLMs into industrial RSs. While most approaches deploy LLMs offline to generate and pre-cache augmented representations for RSs, high-dimensional representations from LLMs introduce substantial storage and computational costs. Thus, it is crucial to compress LLM representations effectively. However, we identify a counterintuitive phenomenon during representation compression: Mid-layer Representation Advantage (MRA), where representations from middle layers of LLMs outperform those from final layers in recommendation tasks. This degraded final layer ren...
| Authors | Yunjia Xi, Menghui Zhu, Jianghao Lin et al. |
|---|---|
| Primary category | cs.IR |
| Published | 20 April 2026 |
| Last updated at source | 21 April 2026 |
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