A Review Of Modern Recommender Systems Using Generative Models (gen-recsys) | Awesome LLM Papers Add your paper to Awesome LLM Papers

Awesome LLM Papers

A collection of research papers on LLMs.

Stay Updated

Contact Sean Moran about this survey or website. Made with Jekyll and Hyde.

A Review Of Modern Recommender Systems Using Generative Models (gen-recsys)

Yashar Deldjoo, Zhankui He, Julian McAuley, Anton Korikov, Scott Sanner, Arnau Ramisa, René Vidal, Maheswaran Sathiamoorthy, Atoosa Kasirzadeh, Silvia Milano . Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2024 – 42 citations

[Other] [Paper]   Search on Google Scholar   Search on Semantic Scholar
Compositional Generalization Image Text Integration Interactive Environments Interdisciplinary Approaches KDD Multimodal Semantic Representation Survey Paper Variational Autoencoders Visual Contextualization

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.

Similar Work