A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)
It synthesizes advancements in generative recommender systems for researchers and practitioners, but is incremental as a review.
This survey reviews how generative models enable novel recommendation tasks by modeling complex data like text and images, highlighting evaluation paradigms and open challenges.
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.