A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)
원문 초록
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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CORONA: A Coarse-to-Fine Framework for Graph-based Recommendation with Large Language Models
NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation
RAG-VisualRec: An Open Resource for Vision- and Text-Enhanced Retrieval-Augmented Generation in Recommendation
A Survey of Context Engineering for Large Language Models
SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation
GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models
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