Personalized recommendation stands as a ubiquitous channel for users to explore information or items aligned with their interests. Nevertheless, prevailing recommendation models predominantly rely on unique IDs and categorical features for user-item matching. While this ID-centric approach has witnessed considerable success, it falls short in comprehensively grasping the essence of raw item contents across diverse modalities, such as text, image, audio, and video. This underutilization of multimodal data poses a limitation to recommender systems, particularly in the realm of multimedia services like news, music, and short-video platforms. The recent surge in pretraining and generation techniques presents both opportunities and challenges in the development of multimodal recommender systems. This tutorial seeks to provide a thorough exploration of the latest advancements and future trajectories in multimodal pretraining and generation techniques within the realm of recommender systems. The tutorial comprises three parts: multimodal pretraining, multimodal generation, and industrial applications and open challenges in the field of recommendation. Our target audience encompasses scholars, practitioners, and other parties interested in this domain. By providing a succinct overview of the field, we aspire to facilitate a swift understanding of multimodal recommendation and foster meaningful discussions on the future development of this evolving landscape.

翻译：个性化推荐已成为用户探索符合其兴趣的信息或内容的重要渠道。然而，现有推荐模型主要依赖唯一标识符和类别特征进行用户-项目匹配。尽管这种以ID为中心的方法取得了显著成功，但在全面理解不同模态（如文本、图像、音频和视频）的原始项目内容本质方面仍存在不足。这种对多模态数据的利用不足限制了推荐系统的发展，尤其是在新闻、音乐和短视频平台等多媒体服务领域。近期预训练与生成技术的兴起为多模态推荐系统的开发带来了机遇与挑战。本教程旨在全面探索推荐系统中多模态预训练与生成技术的最新进展及未来方向。教程包含三部分：多模态预训练、多模态生成以及推荐领域的工业应用与开放挑战。我们的目标受众包括学者、从业者及对该领域感兴趣的其他人士。通过提供该领域的简要概述，我们期望帮助读者快速理解多模态推荐，并促进对这一动态领域未来发展的有意义的讨论。