Recent advances in Large Language Models (LLMs) have been changing the paradigm of Recommender Systems (RS). However, when items in the recommendation scenarios contain rich textual information, such as product descriptions in online shopping or news headlines on social media, LLMs require longer texts to comprehensively depict the historical user behavior sequence. This poses significant challenges to LLM-based recommenders, such as over-length limitations, extensive time and space overheads, and suboptimal model performance. To this end, in this paper, we design a novel framework for harnessing Large Language Models for Text-Rich Sequential Recommendation (LLM-TRSR). Specifically, we first propose to segment the user historical behaviors and subsequently employ an LLM-based summarizer for summarizing these user behavior blocks. Particularly, drawing inspiration from the successful application of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) models in user modeling, we introduce two unique summarization techniques in this paper, respectively hierarchical summarization and recurrent summarization. Then, we construct a prompt text encompassing the user preference summary, recent user interactions, and candidate item information into an LLM-based recommender, which is subsequently fine-tuned using Supervised Fine-Tuning (SFT) techniques to yield our final recommendation model. We also use Low-Rank Adaptation (LoRA) for Parameter-Efficient Fine-Tuning (PEFT). We conduct experiments on two public datasets, and the results clearly demonstrate the effectiveness of our approach.

中文翻译：

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利用大型语言模型进行丰富文本的顺序推荐

大型语言模型 (LLM) 的最新进展正在改变推荐系统 (RS) 的范式。然而，当推荐场景中的项目包含丰富的文本信息时，例如网购中的产品描述或社交媒体上的新闻标题，LLM需要更长的文本来全面描述历史用户行为序列。这给基于 LLM 的推荐系统带来了重大挑战，例如超长限制、大量时间和空间开销以及次优模型性能。为此，在本文中，我们设计了一种利用大型语言模型进行富文本顺序推荐（LLM-TRSR）的新颖框架。具体来说，我们首先建议对用户历史行为进行分段，然后采用基于 LLM 的摘要器来总结这些用户行为块。特别是，从卷积神经网络（CNN）和循环神经网络（RNN）模型在用户建模中的成功应用中汲取灵感，我们在本文中引入了两种独特的摘要技术，分别是分层摘要和循环摘要。然后，我们将包含用户偏好摘要、最近用户交互和候选项目信息的提示文本构建到基于 LLM 的推荐器中，随后使用监督微调（SFT）技术对其进行微调，以产生我们的最终推荐模型。我们还使用低秩适应（LoRA）进行参数高效微调（PEFT）。我们在两个公共数据集上进行了实验，结果清楚地证明了我们方法的有效性。