REPREC：表示驱动的参数高效推荐系统

中文摘要 AI 辅助

大语言模型已被应用于序列推荐，以往工作通过输入条件或大语言模型微调纳入协作和序列信号来改善个性化。但现有方法常依赖大语言模型微调等，增加训练复杂度和部署成本。我们提出REPREC，一个通过轻量级用户表示对齐重新制定基于大语言模型的序列推荐的轻量级框架。它通过轻量级MLP注入器将固定大小的用户嵌入映射到少量学习到的软令牌，仅训练注入器。在多个基准数据集上的详尽实验表明，REPREC始终优于LoRA，与不同预训练序列编码器和大语言模型主干兼容，在不修改预训练组件的情况下实现模块化和生产友好的推荐管道。在所有数据集上，对临时和核心用户的收益尤其明显，突出了REPREC在低数据情况下的有效性。最后，在短提示历史上训练并在更长上下文下评估时，REPREC保持LoRA性能的85 - 100%，同时将每个epoch的训练时间平均减少1.51倍，在推荐质量和计算效率之间实现了有效平衡，适用于生产部署。代码可通过此https URL获取。

英文摘要

Large language models (LLMs) have been applied to sequential recommendation by incorporating collaborative signals through input conditioning or model adaptation. However, existing approaches often require LLM fine-tuning, additional architectural modules, representation distillation, or item-level conditioning over long interaction histories, increasing computational and deployment costs. We propose REPREC, a lightweight framework that conditions a frozen LLM using compact user-level representations. REPREC maps a fixed-size embedding from a frozen sequential encoder into a small set of learned soft tokens through an MLP injector, training only the injector while leaving both pretrained backbones unchanged. Our extensive experiments demonstrate that REPREC consistently improves recommendation performance across different sequential encoders, LLM backbones, and user activity levels. Its compact conditioning mechanism also makes REPREC computationally efficient during both training and inference. Moreover, training with short histories while evaluating with longer contexts retains 94--99\% of full-history performance while achieving an average $1.50\times$ per-epoch training speedup. The code is available at: https://github.com/phdbotcode/REPREC

发表机构

• The Ohio State University（俄亥俄州立大学）
• Capital One, AI Foundations（Capital One人工智能基础部）

机构由 AI 辅助整理，请以论文原文为准。