In recent years, with large language models (LLMs) achieving state-of-the-art performance in context understanding, increasing efforts have been dedicated to developing LLM-enhanced sequential recommendation (SR) methods. Considering that most existing LLMs are not specifically optimized for recommendation tasks, adapting them for SR becomes a critical step in LLM-enhanced SR methods. Though numerous adaptation methods have been developed, it still remains a significant challenge to adapt LLMs for SR both efficiently and effectively. To address this challenge, in this paper, we introduce a novel side sequential network adaptation method, denoted as SSNA, for LLM enhanced SR. SSNA features three key designs to allow both efficient and effective LLM adaptation. First, SSNA learns adapters separate from LLMs, while fixing all the pre-trained parameters within LLMs to allow efficient adaptation. In addition, SSNA adapts the top-a layers of LLMs jointly, and integrates adapters sequentially for enhanced effectiveness (i.e., recommendation performance). We compare SSNA against five state-of-the-art baseline methods on five benchmark datasets using three LLMs. The experimental results demonstrate that SSNA significantly outperforms all the baseline methods in terms of recommendation performance, and achieves substantial improvement over the best-performing baseline methods at both run-time and memory efficiency during training. Our analysis shows the effectiveness of integrating adapters in a sequential manner. Our parameter study demonstrates the effectiveness of jointly adapting the top-a layers of LLMs.

翻译：近年来，随着大语言模型（LLMs）在上下文理解领域取得最优性能，越来越多研究致力于开发LLM增强的序列推荐（SR）方法。鉴于现有大多数LLMs未针对推荐任务进行专门优化，如何将其适配至SR成为LLM增强SR方法的关键环节。尽管已有多种适配方法被提出，但在兼顾效率与效果的前提下适配LLMs至SR仍是一项重大挑战。为应对这一挑战，本文提出一种新型侧边序列网络适配方法SSNA（Side Sequential Network Adaptation），用于LLM增强的SR。SSNA通过三项关键设计实现高效且有效的LLM适配：首先，SSNA学习与LLMs分离的适配器，同时固定LLMs中所有预训练参数以实现高效适配；其次，SSNA联合适配LLMs的顶层a层，并按顺序集成适配器以增强推荐效果（即推荐性能）。我们使用三种LLMs在五个基准数据集上，将SSNA与五种先进基线方法进行对比。实验结果表明，SSNA在推荐性能上显著优于所有基线方法，并在训练时的运行时间效率和内存效率方面均取得显著提升。分析表明，按顺序集成适配器具有有效性；参数研究则证明了联合适配LLMs顶层a层的有效性。