Traditional recommender systems (RS) typically use user-item rating histories as their main data source. However, deep generative models now have the capability to model and sample from complex data distributions, including user-item interactions, text, images, and videos, enabling novel recommendation tasks. This comprehensive, multidisciplinary survey connects key advancements in RS using Generative Models (Gen-RecSys), covering: interaction-driven generative models; the use of large language models (LLM) and textual data for natural language recommendation; and the integration of multimodal models for generating and processing images/videos in RS. Our work highlights necessary paradigms for evaluating the impact and harm of Gen-RecSys and identifies open challenges. This survey accompanies a tutorial presented at ACM KDD'24, with supporting materials provided at: https://encr.pw/vDhLq.
@article{arxiv.2404.00579,
title = {A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)},
author = {Yashar Deldjoo and Zhankui He and Julian McAuley and Anton Korikov and Scott Sanner and Arnau Ramisa and René Vidal and Maheswaran Sathiamoorthy and Atoosa Kasirzadeh and Silvia Milano},
journal= {arXiv preprint arXiv:2404.00579},
year = {2024}
}
Comments
This survey accompanies a tutorial presented at ACM KDD'24