The 1st Workshop on Recommendation with Generative Models

Wenjie Wang, Yong Liu, Yang Zhang, Weiwen Liu, Fuli Feng, Xiangnan He, Aixin Sun

Abstract: The boom of generative models has paved the way for significant advances in recommender systems. For instance, pre-trained generative models offer unprecedented opportunities to improve recommender algorithms for user modeling. This workshop aims to provide a platform for researchers to actively explore and share innovative ideas on integrating generative models into recommender systems, mainly focusing on five key aspects: (i) enhancing recommender algorithms, (ii) generating personalized content in some scenarios such as micro-videos, (iii) changes in the user-system interaction paradigm, (iv) boosting trustworthiness checks, and (v) evaluation methodologies of generative recommendation. With the rapid development of generative models, a growing number of studies along the above directions are emerging, revealing the timeliness and necessity of this workshop. The related research will bring novel features to recommender systems and contribute to new tasks and technologies in both academia and industry. In the long run, this research direction might revolutionize the traditional recommender paradigm and lead to the maturation of next-generation recommender systems.