Overview
The “Agentic and Generative AI for E-Commerce” workshop explores the rapidly evolving intersection of recommender systems, generative AI, and agentic AI in online retail. As AI systems evolve from passive generators to autonomous agents capable of planning, reasoning, and taking actions, e-commerce stands at the forefront of this transformation. The objective of this workshop is to foster discussions on how agentic AI systems — autonomous agents that can browse, compare, negotiate, and purchase on behalf of users — alongside generative models ranging from large language models (LLMs) to diffusion-based techniques, can transform personalization, product recommendations, content creation, and user engagement in e-commerce platforms. Through this workshop, we seek to highlight novel research, industry applications, and emerging trends that can enhance the capabilities of modern recommender systems.
E-commerce companies face challenges such as lack of quality content, subpar user experience, and sparse datasets. Generative and agentic AI offer significant potential to address these — from generating product content to deploying autonomous shopping assistants for end-to-end purchase workflows. Yet, scaling these technologies presents challenges including hallucination, excessive costs, latency, and ensuring safe autonomous agent behavior.
Call for Papers
We welcome papers that leverage Agentic and Generative Artificial Intelligence (Gen AI) in e-commerce. Detailed topics are mentioned in CFP. Papers can be submitted at Easychair. Accepted papers will be published via CEUR-WS as open-access workshop proceedings.
Important Dates
- Call for Papers publication: April 21, 2026
- Paper submission deadline: July 20, 2026
- Reviewer deadline: August 7, 2026
- Author notification: August 14, 2026
- Camera-ready version deadline: August 28, 2026
- Workshop: September 28, 2026 (13:30–17:30)
Schedule
We have a half-day program at Minneapolis, Minnesota, USA.
| Time | Agenda |
|---|---|
| 1:30–1:40 PM | Registration and Welcome |
| 1:40–2:20 PM | Keynote by Patrick Jordan (Microsoft): TBA |
| 2:20–3:00 PM | Keynote by Akshay Soni (Shopify): Foundation Models for Agentic and Counterfactual Decision Support in E-Commerce |
| 3:00–3:30 PM | Coffee Break |
| 3:30–4:10 PM | Keynote by Heng Liu (Meta): LLM Ranking in Facebook Verticals: from Content based LLM Ranking to Unified Generative & Ranking Recommender |
| 4:10–4:25 PM | Paper Presentation 1 |
| 4:25–4:40 PM | Paper Presentation 2 |
| 4:40–5:30 PM | Poster Session |
Keynote Speakers
Patrick Jordan
Title: TBA
Presenter: Patrick Jordan, Microsoft
Description: TBA
Akshay Soni
Title: Foundation Models for Agentic and Counterfactual Decision Support in E-Commerce
Presenter: Akshay Soni, Shopify
Description: Most generative recommendation research models the consumer side, with sequences of a buyer’s clicks, views, and purchases. We describe a foundation model for the merchant side of Shopify, built to support agents that plan and act on a shop’s behalf.
The modeled entity is a shop and the sequence is its full operating history, spanning high-frequency behavior (such as sessions and checkouts), pre-computed aggregates (such as GMV summaries), and sparse lifecycle events (such as subscription and churn changes). Catalog entities such as products and pages are tokenized into semantic IDs (SIDs) via residual-quantized autoencoding, compressing a high-cardinality space into a compact vocabulary shared across similar entities, which curbs sparsity and cold-start.
A Hierarchical Sequential Transduction Unit (HSTU) backbone is trained autoregressively, combining next-token prediction with a multi-horizon future-token-set objective. The shared representation yields general-purpose merchant embeddings that feed a growing set of downstream applications, from recommendation to forecasting, and enable reasoning about the effect of interventions: a hypothetical action is inserted into a shop’s sequence, and the model predicts the events that would follow, simulating the outcome of taking that action, such as adopting a paid-marketing channel. This grounds autonomous decisions in observed behavior and lets actions be evaluated before deployment.
Heng Liu
Title: LLM Ranking in Facebook Verticals: from Content based LLM Ranking to Unified Generative & Ranking Recommender
Presenter: Heng Liu, Meta
Description: In this talk, I’ll start with content-based LLM ranking, focusing on how incorporating user preferences (explicit signals and inferred interests) can substantially improve relevance—especially in sparse or cold-start settings where behavioral data is limited. I’ll then transition to Project Reno, a next-generation recommender built around a single unified LLM backbone that operates in both ranking mode (high-precision scoring/reranking) and generative mode (high-recall discovery and next-item recommendation). I’ll cover key modeling ideas such as hierarchical discrete item representations, co-pretraining for item–text alignment, and downstream adaptation via multi-task SFT and RL, and share practical lessons and measured impact from deployments across Jobs, Search, and related verticals.
Accepted Papers
Organizers
Mansi Mane
Walmart
Neeti Narayan
Amazon
Djordje Gligorijevic
Meta
Dingxian Wang
Upwork
Topojoy Biswas
Walmart
Claudio Pomo
Politecnico di Bari
Contact
For any questions, please email genai-ecommerce@googlegroups.com.