Sequential recommendation problems have received increasing attention in research during the past few years, leading to the inception of a large variety of algorithmic approaches. 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 devise and evaluate three approaches to leverage the power of LLMs in different ways. Our results from experiments on two datasets show that initializing the state-of-the-art sequential recommendation model BERT4Rec with embeddings obtained from an LLM improves NDCG by 15-20% compared to the vanilla BERT4Rec model. Furthermore, we find that a simple approach that leverages LLM embeddings for producing recommendations, can provide competitive performance by highlighting semantically related items. We publicly share the code and data of our experiments to ensure reproducibility.

翻译：近年来，序列推荐问题在研究中受到越来越多关注，由此催生了大量算法方法的涌现。本文探索了如何利用当今在许多AI应用中引发颠覆性效应的大型语言模型（LLMs）来构建或改进序列推荐方法。具体而言，我们设计并评估了三种以不同方式利用LLM能力的方法。在两个数据集上的实验结果表明，使用从LLM获得的嵌入向量初始化最先进的序列推荐模型BERT4Rec，相比原始BERT4Rec模型可将NDCG提高15%-20%。此外，我们发现一种利用LLM嵌入生成推荐的简单方法能够通过突出语义相关项来展现具有竞争力的性能。我们公开共享了实验代码与数据以确保可重复性。