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：大语言模型（LLMs）的出现已在自然语言处理领域取得巨大成功，这得益于其多样化的训练范式，使LLMs能够有效捕捉复杂的语言模式和语义表征。特别是近期提出的"预训练-提示-预测"训练范式作为在有限标注数据条件下学习可泛化模型的方法备受关注。顺应这一进展，这些训练范式近期已被适配至推荐领域，并被视为学术界与工业界极具前景的研究方向。本半日教程旨在系统阐释如何通过不同训练范式从预训练模型中提取与迁移知识，从通用性、稀疏性、有效性和可信度等多维度改进推荐系统。教程将首先介绍面向推荐的语言建模范式基础概念与通用架构，随后聚焦LLM相关训练策略与优化目标在各类推荐任务中的最新适配进展，继而系统阐述基于LLM的推荐系统中的伦理问题及其评估缓解方法，并总结相关数据集、评估指标以及关于训练范式推荐性能的实证研究。最后，我们将通过探讨开放挑战与未来方向作为教程总结。