---
title: "Toward Trustworthy Recommender Systems in the Era of Agentic AI: From Relational User Modeling to Generative Personalization"
authors:
  - "Wenqi Fan"
doi: "10.24963/ijcai.2026/905"
url: "https://ijcai.org/proceedings/2026/905"
pdf: "https://ijcai.org/proceedings/2026/0905.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "8155-8160"
track: "Early Career Spotlight"
keywords:
  - "AI: Agent-based and Multi-agent Systems"
  - "AI: Data Mining"
---

# Toward Trustworthy Recommender Systems in the Era of Agentic AI: From Relational User Modeling to Generative Personalization

Wenqi Fan

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 8155-8160. DOI: [10.24963/ijcai.2026/905](https://doi.org/10.24963/ijcai.2026/905)

## Abstract

Recommender systems are evolving from models that predict user–item matching scores into intelligent systems that learn from relational structure, retrieve  evidence, reason over user intent, and support personalized actions. This talk discusses a research agenda for trustworthy recommender systems in the era of agentic AI, organized around a transition from relational user modeling to generative personalization. We will discuss three connected components: relational user modeling with deep neural networks, generative and agentic personalization with large language models, and trustworthy foundation for reliable  recommendations. The discussion revisits how social, behavioral, and knowledge relations can be integrated for user modeling; how generative models and personalized agents expand recommendation from ranking to intelligent decision support; and how trustworthiness challenges should be addressed throughout the system. The talk aims to provide a unified view of recommender systems as relational, generative, agentic, and trustworthy AI systems.