Recommender Systems are built to retrieve relevant items to satisfy users' information needs. The candidate corpus usually consists of a finite set of items that are ready to be served, such as videos, products, or articles. With recent advances in Generative AI such as GPT and Diffusion models, a new form of recommendation task is yet to be explored where items are to be created by generative models with personalized prompts. Taking image generation as an example, with a single prompt from the user and access to a generative model, it is possible to generate hundreds of new images in a few minutes. How shall we attain personalization in the presence of "infinite" items? In this preliminary study, we propose a two-stage framework, namely Prompt-Model Retrieval and Generated Item Ranking, to approach this new task formulation. We release GEMRec-18K, a prompt-model interaction dataset with 18K images generated by 200 publicly-available generative models paired with a diverse set of 90 textual prompts. Our findings demonstrate the promise of generative model recommendation as a novel personalization problem and the limitations of existing evaluation metrics. We highlight future directions for the RecSys community to advance towards generative recommender systems. Our code and dataset are available at https://github.com/MAPS-research/GEMRec.

翻译：推荐系统旨在检索相关项目以满足用户的信息需求。候选语料库通常由一组有限且可立即提供的项目组成，例如视频、产品或文章。随着GPT和扩散模型等生成式人工智能的最新进展，一种新的推荐任务形式尚待探索：项目将由生成模型通过个性化提示生成。以图像生成为例，仅需用户提供单一提示并访问生成模型，即可在几分钟内生成数百张新图像。在存在“无限”项目的情况下，我们如何实现个性化？在本初步研究中，我们提出了一种两阶段框架，即提示-模型检索与生成项目排序，以应对这一新任务形式。我们发布了GEMRec-18K数据集，该数据集包含由200个公开可用生成模型与90个多样化文本提示配对生成的18,000张图像。我们的研究结果表明，生成式模型推荐作为一种新的个性化问题具有潜力，但现有评估指标存在局限性。我们为RecSys社区指出了向生成式推荐系统迈进未来的方向。我们的代码和数据集可在https://github.com/MAPS-research/GEMRec获取。