A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)
<|reference_start|>A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys): 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.<|reference_end|>
@article{deldjoo2024a,
    title={A Review of Modern Recommender Systems Using Generative Models
  (Gen-RecSys)},
    author={Yashar Deldjoo, Zhankui He, Julian McAuley, Anton Korikov, Scott
  Sanner, Arnau Ramisa, Ren'e Vidal, Maheswaran Sathiamoorthy, Atoosa
  Kasirzadeh, Silvia Milano},
    journal={arXiv preprint arXiv:2404.00579},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00579},
    primaryClass={cs.IR cs.AI}
}