Sequential recommendation systems predict the next interaction item based on users' past interactions, aligning recommendations with individual preferences. Leveraging the strengths of Large Language Models (LLMs) in knowledge comprehension and reasoning, recent approaches are eager to apply LLMs to sequential recommendation. A common paradigm is converting user behavior sequences into instruction data, and fine-tuning the LLM with parameter-efficient fine-tuning (PEFT) methods like Low-Rank Adaption (LoRA). However, the uniform application of LoRA across diverse user behaviors is insufficient to capture individual variability, resulting in negative transfer between disparate sequences. To address these challenges, we propose Instance-wise LoRA (iLoRA). We innovatively treat the sequential recommendation task as a form of multi-task learning, integrating LoRA with the Mixture of Experts (MoE) framework. This approach encourages different experts to capture various aspects of user behavior. Additionally, we introduce a sequence representation guided gate function that generates customized expert participation weights for each user sequence, which allows dynamic parameter adjustment for instance-wise recommendations. In sequential recommendation, iLoRA achieves an average relative improvement of 11.4\% over basic LoRA in the hit ratio metric, with less than a 1\% relative increase in trainable parameters. Extensive experiments on three benchmark datasets demonstrate the effectiveness of iLoRA, highlighting its superior performance compared to existing methods in mitigating negative transfer and improving recommendation accuracy. Our data and code are available at https://github.com/AkaliKong/iLoRA.

翻译：序列推荐系统依据用户历史交互行为预测下一交互项目，使推荐结果与个体偏好相匹配。近年来，研究者尝试利用大语言模型（LLMs）在知识理解与推理方面的优势，将其应用于序列推荐任务。主流范式通常将用户行为序列转化为指令数据，并采用参数高效微调方法（如低秩自适应LoRA）对LLM进行微调。然而，在不同用户行为上统一应用LoRA难以充分捕捉个体差异性，导致相异序列间产生负迁移效应。为应对这些挑战，本文提出实例级LoRA（iLoRA）。我们创新性地将序列推荐任务视为多任务学习问题，将LoRA与混合专家（MoE）框架相结合。该方法促使不同专家捕获用户行为的多样化特征。此外，我们设计了序列表征引导的门控函数，为每个用户序列生成定制化的专家参与权重，从而实现面向实例级推荐的动态参数调整。在序列推荐任务中，iLoRA在命中率指标上较基础LoRA平均获得11.4%的相对提升，而可训练参数仅增加不足1%。在三个基准数据集上的大量实验验证了iLoRA的有效性，其通过缓解负迁移现象并提升推荐准确性，展现出优于现有方法的性能。相关数据与代码已公开于https://github.com/AkaliKong/iLoRA。