The rise of generative models has driven significant advancements in recommender systems, leaving unique opportunities for enhancing users' personalized recommendations. This workshop serves as a platform for researchers to explore and exchange innovative concepts related to the integration of generative models into recommender systems. It primarily focuses on five key perspectives: (i) improving recommender algorithms, (ii) generating personalized content, (iii) evolving the user-system interaction paradigm, (iv) enhancing trustworthiness checks, and (v) refining evaluation methodologies for generative recommendations. With generative models advancing rapidly, an increasing body of research is emerging in these domains, underscoring the timeliness and critical importance of this workshop. The related research will introduce innovative technologies to recommender systems and contribute to fresh challenges in both academia and industry. In the long term, this research direction has the potential to revolutionize the traditional recommender paradigms and foster the development of next-generation recommender systems.

翻译：生成模型的崛起为推荐系统带来了显著进步，为提升用户个性化推荐创造了独特机遇。本研讨会旨在为研究人员搭建探索与交流生成模型与推荐系统融合创新理念的平台，主要聚焦五大核心方向：（i）改进推荐算法、（ii）生成个性化内容、（iii）演变用户-系统交互范式、（iv）增强可信度验证，以及（v）优化生成式推荐评估方法。随着生成模型快速发展，上述领域的研究成果日益增多，充分彰显了本研讨会的及时性与关键重要性。这些相关研究将为推荐系统引入创新技术，并在学术界与工业界催生全新挑战。从长远来看，该研究方向有潜力颠覆传统推荐范式，推动下一代推荐系统的发展。