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.
The primary aim of this workshop is to foster innovative research centered around the integration of generative models with recommender systems, specifically focusing on five key aspects. First, the issue will encourage active researchers to leverage generative models to improve recommender algorithms for better user modeling. Second, it encourages exploring the possibility of using generative models to produce more diverse content in some scenarios, to supplement human-generated content for meeting users' wide-ranging preferences and information needs. Third, it welcomes major innovations in the way in which users interact with recommender systems, enabled by advanced language understanding and generation abilities of generative models in general and of large language models in particular. Fourth, the workshop will emphasize the importance of trustworthiness when using generative models for recommendation, including but not limited to examining the trustworthiness of generated content, addressing biases in recommender algorithms, and ensuring compliance with emerging ethical and legal standards. Last but not least, the workshop will encourage researchers to design evaluation methodologies to examine the usage of generative models in recommender systems. This involves the development of new evaluation metrics and standards, as well as the establishment of human evaluation paradigms and interfaces.
The workshop will serve as an invaluable platform for researchers to contribute the latest ideas, advances, and breakthroughs in this rapidly evolving field. We invite original submissions on recommender systems with generative models, including but not limited to the following topics:
Submitted papers (.pdf format) must use the template of ACM CIKM 2023. Please remember to add Concepts and Keywords. Submissions can be of varying length from 4 to 8 pages, plus additional pages for the reference pages, i.e., the reference page(s) are not counted to the page limit of 4 to 8 pages. There is no distinction between long and short papers, but the authors may decide on the appropriate length of the paper. All papers will undergo the same review process and review period. Paper submissions must conform to the ``double-blind'' review policy. All papers will be peer-reviewed by experts in the field. Acceptance will be based on relevance to the workshop, scientific novelty, and technical quality.
Submission site: https://easychair.org/conferences/?conf=genrec23. We are also preparing an ACM TOIS special issue on using generative models for recommendation. High-quality submissions will be recommended to submit to this special issue.
