Overview
The “Gen AI for E-commerce” workshop explores the role of Generative Artificial Intelligence in transforming e-commerce through enhanced user experience and operational efficiency. E-commerce companies grapple with multiple challenges such as lack of quality content for products, subpar user experience, sparse datasets etc. Gen AI offers significant potential to address these complexities. Yet, deploying these technologies at scale presents challenges such as hallucination in data, excessive costs, increased latency response, and limited generalization in sparse data environments. This workshop will bring together experts from academia and industry to discuss these challenges and opportunities, aiming to showcase case studies, breakthroughs, and insights into practical implementations of Gen AI in e-commerce.
Call for papers
We will welcome papers that leverage Generative Artificial Intelligence (Gen AI) in e-commerce. Detailed topics are mentioned in CFP. Papers can be submitted at Easychair.
Information for the day of the workshop
Workshop at RecSys2025
- Paper submission deadline: August 7, 2025
- Paper acceptance notification: August 25, 2025
- Camera-ready manuscript deadline: September 4, 2025
- Workshop: September 22, 2025
Schedule
We have a half-day program on September 22 at Prague, Czech Republic.
| Time (CET) | Agenda |
|---|---|
| 9:00-9:10am | Registration and Welcome |
| 9:10-9:50am | Keynote by Xia Ning : Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data |
| 9:50-10:30am | Paper Presentations |
| 10:30-11:00am | Coffee Break |
| 11:00-11:40am | Panel Discussion |
| 11:40-12:00pm | Closing Remarks |
Keynote Speakers
Xia Ning
Ohio State University
Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data
Abstract: With tremendous efforts in developing effective e-commerce models, conventional e-commerce models show limited success in generalist e-commerce modeling, and suffer from unsatisfactory performance on new users and new products – a typical out-of-domain generalization challenge. Meanwhile, large language models (LLMs) demonstrate outstanding performance in generalist modeling and out-of-domain generalizability in many fields. Toward fully unleashing their power for e- commerce, in this talk, I will present ECInstruct, the first open-sourced, large-scale, and high-quality benchmark instruction dataset for e-commerce. Leveraging ECInstruct, we develop eCeLLM, a series of e-commerce LLMs, by instruction-tuning general-purpose LLMs. Our comprehensive experiments and evaluation demonstrate that eCeLLM models substantially outperform baseline models, including the most advanced GPT-4 and the state-of-the-art task-specific models in in-domain evaluation. Moreover, eCeLLM exhibits excellent generalizability to out of domain settings, including unseen products and unseen instructions, highlighting its superiority as a generalist e-commerce model. Both the ECInstruct dataset and the eCeLLM models show great potential in empowering versatile and effective LLMs for e- commerce. ECInstruct and eCeLLM models are publicly accessible through https://ninglab.github.io/eCeLLM/.
Bio
Bio: Dr. Xia Ning is a Professor in the Biomedical Informatics Department (BMI), and the Computer Science and Engineering Department, The Ohio State University. She also holds a courtesy appointment with the Division of Medicinal Chemistry and Pharmacognosy, College of Pharmacy at OSU. She is the Section Chief of AI, Clinical Informatics, and Implementation Science at BMI, and the Associate Director of Biomedical Informatics at OSU Center for Clinical and Translational Science Institute (CTSI). She received her Ph.D. in Computer Science and Engineering from the University of Minnesota, Twin Cities, in 2012. Ning’s research is on Artificial Intelligence (AI) and Machine Learning with applications in health care and e-commerce. Her work on “SLIM: Sparse linear methods for top-n recommender systems” received the 10-Years-Highest-Impact-Paper Award, IEEE International Conference on Data Mining (ICDM) in 2020.
Accepted Papers
- Dummy Paper for GenAIEcommerce 2025
Sample AuthorAbstractAbstract: This is a placeholder paper for the GenAIEcommerce 2025 workshop. The actual accepted papers will be added here once they are available.PDF Code
Organizers
Mansi Mane
Walmart Global Tech
Bio: Mansi Mane is Staff Data Scientist at Search and Recommendation team in Walamrt Labs. 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 lead 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.
Djordje Gligorijevic
eBay
Bio: Djordje Gligorijevic is applied sciences manager at eBay, leading allocation and pricing team in eBay’s sponsored search program. Prior to eBay Djordje worked as 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.
Dingxian Wang
Upwork
Bio: Dingxian is a Applied Science leader with around 10 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 1500 citations, ICT Research Project of the Year 2021 of ACS (Australian Computer Society) and eBay Leaders’ Choice Award.
Topojoy Biswas
Walmart Global Tech
Bio: Topojoy Biswas is Distinguished Data Scientist at Walmart Labs. 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.
Evren Korpeoglu
Walmart Global Tech
Bio: Evren Korpeoglu is a Director of Data Science at Personalization and Recommendations team in Walmart Global Tech. At Walmart he leads efforts related to whole page optimization, item recommendations as well as using Generative AI based models for recommendations. He completed his Ph.D. from Bikent University. He has published at international conferences like NeurIPS, ICML, SIGKDD.
Yongfeng Zhang
Rutgers University
Bio: Yongfeng Zhang is an Assistant Professor in the Department of Computer Science at Rutgers University (The State University of New Jersey). His research interest is in Information Retrieval, Economics of Data Science, Explainable AI, Fairness in AI and AI Ethics. In the previous he was a postdoc advised by Prof. W. Bruce Croft in the Center for Intelligent Information Retrieval (CIIR) at UMass Amherst, and did his PhD and BE in Computer Science at Tsinghua University, with a BS in Economics at Peking Univeristy. He is a Siebel Scholar of the class 2015. Together with coauthors, he has been consistently working on explainable search and recommendation models, fairness-aware machine learning, conversational search and recommendation, neural logic/symbolic reasoning, economic machine learning , efficient/robust machine learning, knowledge graph embedding, legal/medical retrieval, as well as causal/counterfactual models for information retrieval. His recent research on causality in search and recommendation include causal collaborative filtering, causal explainable recommendation, counterfactual debiasing models, and causal models for mitigating feedback loops in IR systems.
Marios Savvides
CMU, UltronAI
Bio: Professor Marios Savvides is the Bossa Nova Robotics Professor of Artificial Intelligence at Carnegie Mellon University and is also the Founder and Director of the Biometrics Center at Carnegie Mellon University and a Full Tenured Professor in the Electrical and Computer Engineering Department. He received his Bachelor of Engineering in Microelectronics Systems Engineering from University of Manchester Institute of Science and Technology in 1997 in the United Kingdom, his Master of Science in Robotics from the Robotics Institute in 2000 and his PhD from the Electrical and Computer Engineering department at CMU in 2004. He has authored and co-authored over 250 journal and conference publications, including 22 book chapters and served as the area editor of the Springer’s Encyclopedia of Biometrics. Some of his notable accomplishments include developing a 40ft stand-off distance iris recognition system, robust face detection even in presence of extreme occlusions, a fully autonomous AI inventory robotic image analytics system for detecting out-of-stocks that he and his team scaled to 550 walmart retail stores. His latest research is in large foundation vision models for zero shot enrollment for robust object recognition which has spun out as the enterprise software company UltronAI, Inc. His work was presented at the World Economic Forum (WEF) in Davos, Switzerland in January 2018 and his research has been featured in over 100 news media articles (CNN, CBS 60 minutes, Scientific American, Popular Mechanics etc). He is the recipient of CMU’s 2009 Carnegie Institute of Technology (CIT) Outstanding Research Award, the Gold Award in the 2015 Edison Awards in Applied Technologies for his biometrics work, 2018 Global Pittsburgh Immigrant Entrepreneur Award in Technological Innovation, the 2020 Artificial Intelligence Excellence Award in “Theory of Mind”, the Gold Award in 2020 Edison Awards for Retail Innovations on Autonomous Data Capture and Analysis of On-Shelf Inventory, the “Stevens J. Fenves Award for Systems Research”, the “2020 Outstanding Contributor to AI” award from the US Secretary of the Army Mr. Ryan McCarthy, named the “Inventor of Year” by the Pittsburgh Intellectual Property Law Association (PIPLA), 2022.
Julian McAuley
Walmart Global Tech
Bio: McAuley is a professor in CSE. His research focuses on the linguistic, temporal, and social dimensions of opinions and behavior in social networks and other online communities. He has harnessed data science tools to increase understanding the facets of people’s opinions, the processes that lead people to acquire taste for gourmet foods and beers, and even the visual dimensions of how they make fashion choices. He has gained academic, industry and media attention for his work analyzing massive volumes of user data from online social communities including Amazon, Yelp, Facebook and BeerAdvocate. His work includes using artificial intelligence in fashion choice, and data science in developing models that generate step-charts for the globally popular videogame, Dance Dance Revolution.
Program Committee
- Sample PC Member (Sample University)