GEMRec: Towards Generative Model Recommendation

TL;DR

This paper introduces GEMRec, a framework for recommending generative models based on user prompts, and releases a dataset to facilitate research in this emerging personalized generative AI recommendation task.

Contribution

It proposes a novel two-stage framework for generative model recommendation and releases the GEMRec-18K dataset for future research in personalized generative AI systems.

Findings

Generative model recommendation is a promising new personalization challenge.

Existing evaluation metrics have limitations in this context.

The dataset enables benchmarking and further exploration of generative recommender systems.

Abstract

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.