About the tutorial
Modern recommender systems have made major progress, but many large-scale, user-facing solutions still behave like static “one-shot” recommenders. As user expectations shift toward interactive, context-aware, and adaptive experiences, recommender systems increasingly need to support multi-step reasoning, tool use, memory, and autonomous orchestration.
This tutorial focuses on how recent advances in large language models (LLMs) enable a new class of recommender systems: agentic (and often multi-agent) systems that can:
- reason over evolving user needs,
- interact through dialog and longer context,
- call tools and external APIs,
- orchestrate multi-step workflows,
- refine outputs using constraints and feedback,
- and support practical production requirements (reliability, scalability, transparency, safety).
Examples and patterns discussed include context-aware recommendation, dynamic multi-step orchestration, and personalized recommendation pipelines, culminating in a hands-on session that bridges concepts with implementation.
Instructors
| Instructor | Affiliation |
|---|---|
| Reza Yousefi Maragheh | Walmart Global Tech |
| Yashar Deldjoo | Polytechnic University of Bari |
| Chi Wang | Google DeepMind |
| Jason Cho | Walmart Global Tech |
| Derek Cheng | Google DeepMind |
What you’ll learn
By the end of this tutorial, you should be able to:
- Understand the shift from traditional recommenders to LLM-powered, interactive, agentic systems
- Recognize the core building blocks (“alphabets”) of multi-agentic systems, including:
- memory types and retrieval strategies
- function calling and tool usage
- orchestration protocols / interfaces
- reasoning load balancing across steps or agents
- Apply common agentic RecSys design patterns for:
- conversational recommendation
- context-aware autonomous recommendation
- recommendation evaluation and user simulation
- explanation generation
- Gain hands-on familiarity with frameworks commonly used for agentic pipelines (e.g., multi-agent and orchestration frameworks)
- Identify practical pitfalls and open challenges such as:
- scalability and latency constraints
- hallucinations and error propagation
- transparency, fairness, bias, and privacy risks
Who this is for
This tutorial is designed for:
- PhD students and researchers exploring agentic systems for recommendation
- Senior researchers and practitioners working with generative/LLM-based RecSys
- Industry teams looking for practical patterns to move from prototypes to scalable systems
Materials and companion resources
- Slides (PDF) — full tutorial deck
- architecture diagrams
Citation
If you find this tutorial useful in your research or work, feel free to cite our tutorial:
@inproceedings{yousefi2025multi,
title={Multi-Agentic Recommender Systems: Foundations, Design Patterns, and E-Commerce Applications—An Industrial Tutorial},
author={Yousefi Maragheh, Reza and Deldjoo, Yashar and Wang, Chi and Cho, Jason and Cheng, Derek},
booktitle={Proceedings of the Nineteenth ACM Conference on Recommender Systems},
pages={1427--1429},
year={2025}
}
Yousefi Maragheh, R., Deldjoo, Y., Wang, C., Cho, J., & Cheng, D. (2025). Multi-Agentic Recommender Systems: Foundations, Design Patterns, and E-Commerce Applications—An Industrial Tutorial. In Proceedings of the Nineteenth ACM Conference on Recommender Systems (pp. 1427–1429).