The sequential recommendation problem has attracted considerable research attention in the past few years, leading to the rise of numerous recommendation models. In this work, we explore how Large Language Models (LLMs), which are nowadays introducing disruptive effects in many AI-based applications, can be used to build or improve sequential recommendation approaches. Specifically, we design three orthogonal approaches and hybrids of those to leverage the power of LLMs in different ways. In addition, we investigate the potential of each approach by focusing on its comprising technical aspects and determining an array of alternative choices for each one. We conduct extensive experiments on three datasets and explore a large variety of configurations, including different language models and baseline recommendation models, to obtain a comprehensive picture of the performance of each approach. Among other observations, we highlight that initializing state-of-the-art sequential recommendation models such as BERT4Rec or SASRec with embeddings obtained from an LLM can lead to substantial performance gains in terms of accuracy. Furthermore, we find that fine-tuning an LLM for recommendation tasks enables it to learn not only the tasks, but also concepts of a domain to some extent. We also show that fine-tuning OpenAI GPT leads to considerably better performance than fine-tuning Google PaLM 2. Overall, our extensive experiments indicate a huge potential value of leveraging LLMs in future recommendation approaches. We publicly share the code and data of our experiments to ensure reproducibility.

翻译：序列推荐问题在过去几年吸引了大量研究关注，催生了众多推荐模型。本文探讨了当前在诸多人工智能应用中引发颠覆性影响的大语言模型如何用于构建或改进序列推荐方法。具体而言，我们设计了三种正交方法及其混合策略，以不同方式利用大语言模型的能力。同时，我们通过关注每种方法的技术构成要素并为其确定一系列替代方案，深入探究了各方法的潜力。我们在三个数据集上开展了广泛实验，探索了包括不同语言模型和基线推荐模型在内的大量配置，以全面把握各方法的性能表现。观测发现，将大语言模型生成的嵌入向量用于初始化BERT4Rec或SASRec等前沿序列推荐模型，可在准确率方面带来显著性能提升。此外，针对推荐任务微调大语言模型不仅能使其掌握任务本身，还能在一定程度上习得领域概念。我们还发现，微调OpenAI GPT的性能明显优于微调Google PaLM 2。总体而言，我们的广泛实验表明，将大语言模型应用于未来推荐方法具有巨大潜力。为确保可复现性，我们已公开共享实验代码和数据。