A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)

Yashar Deldjoo, Zhankui He, Julian McAuley, Anton Korikov, Scott Sanner +5 more
3/31/2024
cs.IRcs.AI

No official code found. Best implementation: yasdel/Gen-RecSys (90 stars, last commit 2024-09-20).

Abstract

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.

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Code Implementations(1)

yasdel/Gen-RecSysCommunity100%
906Dec 1, 20232 years ago

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Cite this paper

@article{deldjoo2024review,
  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},
  year   = {2024},
  eprint = {2404.00579},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.00579v2}
}

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