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.
Important Dates
- Call for papers publicized: April 2026
- Easychair portal open: May 5, 2026
- Paper submission deadline: July 20, 2026
- Paper acceptance notification: August 15, 2026
- Camera-ready manuscript deadline: September 5, 2026
- Workshop: September 28 – October 2, 2026
Schedule
We have a half-day program at Minneapolis, Minnesota, USA.
| Time |
Agenda |
| 1:30–1:40 PM |
Registration and Welcome |
| 1:40–2:30 PM |
Keynote 1 |
| 2:30–2:45 PM |
Paper Presentation 1 |
| 2:45–3:00 PM |
Paper Presentation 2 |
| 3:00–3:15 PM |
Paper Presentation 3 |
| 3:15–3:45 PM |
Coffee Break |
| 3:45–4:35 PM |
Keynote 2 |
| 4:35–5:30 PM |
Poster Session |
Keynote Speakers
To be announced.
Accepted Papers
Organizers
Bio
Bio: Mansi Mane is Staff Machine Learning Scientist at Search and Recommendation team in Walmart. She was the main organizer for the first and second workshop for Generative AI for E-commerce. She completed her Masters from Carnegie Mellon University in 2018. She currently focuses on research and development of machine learning algorithms for recommendations, search, marketing as well as content generation. Mansi was previously Applied Scientist at AWS where she led efforts for training of billion scale large language models from scratch. Her research interests include machine learning, multimodal LLMs pretraining, fine-tuning as well as in-context learning. She has published papers in ICML, RecSys, WWW conferences.
Bio
Bio: Neeti Narayan is a Senior Applied Scientist at Amazon, leading efforts focusing on GenAI-based commerce content, personalization, and product recommendation systems that have driven millions of dollars in revenue. She also organizes workshops at Amazon’s internal Machine Learning Conference (AMLC) on the application of generative AI in advertising, and actively contributes to broader scientific community through paper reviews and collaborative research. Prior to Amazon, Neeti held a research position at Yahoo, where she implemented and deployed large-scale email classification and user action prediction models processing billions of emails every day. She holds a Ph.D. degree from SUNY Buffalo (2018). Her interests span multimodal LLMs, NLP, and computer vision. Neeti has published in conferences and journals such as CVPR, ISBA, and Image and Vision Computing.
Djordje Gligorijevic
Meta
Bio
Bio: Djordje Gligorijevic is applied sciences manager at Meta, leading Intelligent Harvesting team in Meta’s Ranking AI Monetization organization focused on developing model templates with state-of-the-art ML techniques and agentic model optimization for the entire ads ecosystem. Previously he worked as Applied Research Manager at eBay, and as a Research Scientist in Yahoo Research. He received the Ph.D. degree from Temple University, Philadelphia, PA, in 2018. His research interests include Machine Learning, Extreme Multi-Label Classification, NLP, LLMs, and the Integration of Qualitative Knowledge into predictive models with applications in domains of Computational Healthcare, Computational Advertising, Search, Ranking, and Recommendation Systems. Djordje has published at international conferences such as AAAI, KDD, TheWebConf, SDM, CIKM, SIGIR, as well as in international journals like Data Mining and Knowledge Discovery, BigData journal where he serves as associate editor, Methods and Nature’s Scientific Reports.
Bio
Bio: Dingxian is an Applied Science leader with around 12 years industry experience at the intersection of machine learning, software engineering, applied science, and product development. He is passionate about applying skills to solving real-world problems, especially in the field of technology and data science. He is currently leading a team focus on the ranking, personalization and recommendation in the search area. Throughout the career, Dingxian has been involved in a wide range of areas, including search engine, query understanding, recall system, ranking system, recommender system, marketing science, personalization, information extraction, knowledge graph etc. With massive proven track records of delivering great business results, and drove hundreds of millions of dollars in GMV and revenue growth. Dingxian has received many top honors and awards ranging from top conference, journals, patents to top research projects as well as internal competition awards. Including 20+ papers on top conference and journals (one best paper candidate of CIKM 2021), 9 US patents, over 2500 citations, ICT Research Project of the Year 2021 of ACS (Australian Computer Society), Upwork All Star Award and eBay Leaders’ Choice Award.
Bio
Bio: Topojoy Biswas is Distinguished Data Scientist at Walmart. At Walmart he leads efforts related to W+ membership models and creative generation projects. Prior to Walmart he worked as Principal Engineer at Yahoo Research where he worked on information extraction on text and videos in Yahoo Knowledge Graph which powers search and information organization in products in Yahoo, like Finance, Sports, entity search and browse. Before Yahoo Knowledge graphs, he worked for Yahoo shopping on attribute extraction and classification of shopping feeds into large taxonomies of products. Topojoy has published in multiple international conferences such as ICIP, ACM Multimedia etc and has spoken on applied machine learning topics in MLConf, KGC etc.
Claudio Pomo
Politecnico di Bari
Bio
Bio: Claudio Pomo is an assistant professor at Politecnico di Bari working on responsible AI for personalization, with a focus on reproducibility and evaluation in recommender systems. He has published at venues such as SIGIR, RecSys, ECIR, and UMAP, and in journals including ACM TORS, TKDE, Information Sciences, and IP&M. He organized the “EvalRS 2023” workshop at KDD, chaired the RecSys Challenge in 2024 and 2025, and co-organized the first edition of “DaQuaMRec” workshop at RecSys 2025.
