The emergence of Large Language Models (LLMs) has achieved tremendous success in the field of Natural Language Processing owing to diverse training paradigms that empower LLMs to effectively capture intricate linguistic patterns and semantic representations. In particular, the recent "pre-train, prompt and predict" training paradigm has attracted significant attention as an approach for learning generalizable models with limited labeled data. In line with this advancement, these training paradigms have recently been adapted to the recommendation domain and are seen as a promising direction in both academia and industry. This half-day tutorial aims to provide a thorough understanding of extracting and transferring knowledge from pre-trained models learned through different training paradigms to improve recommender systems from various perspectives, such as generality, sparsity, effectiveness and trustworthiness. In this tutorial, we first introduce the basic concepts and a generic architecture of the language modeling paradigm for recommendation purposes. Then, we focus on recent advancements in adapting LLM-related training strategies and optimization objectives for different recommendation tasks. After that, we will systematically introduce ethical issues in LLM-based recommender systems and discuss possible approaches to assessing and mitigating them. We will also summarize the relevant datasets, evaluation metrics, and an empirical study on the recommendation performance of training paradigms. Finally, we will conclude the tutorial with a discussion of open challenges and future directions.

翻译：大型语言模型（LLM）的出现因其多样化的训练范式在自然语言处理领域取得了巨大成功，这些范式使LLM能够有效捕捉复杂的语言模式和语义表示。特别是近期"预训练、提示与预测"的训练范式作为一种利用有限标注数据学习可泛化模型的方法，引起了广泛关注。顺应这一发展趋势，这些训练范式最近已被引入推荐领域，并在学术界与工业界被视为一个前景广阔的研究方向。本次半日教程旨在全面阐述如何从通过不同训练范式学习的预训练模型中提取并迁移知识，以从通用性、稀疏性、有效性和可信性等多维度改进推荐系统。教程首先将介绍用于推荐目的的语言建模范式的基本概念与通用架构；随后重点探讨针对不同推荐任务适配LLM相关训练策略与优化目标的最新进展；接着系统分析基于LLM的推荐系统中存在的伦理问题，并探讨评估与缓解这些问题的可行路径。我们还将汇总相关数据集、评估指标，以及关于训练范式推荐性能的实证研究。最后，教程将以开放挑战与未来方向的讨论作为总结。