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 CIKM2024

  • Paper submission deadline: 16th August 2024
  • Paper acceptance notification: 30 August 2024
  • Workshop: 25 October 2024

Keynote Speakers

Himabindu Lakkaraju

Himabindu Lakkaraju

Harvard University
Title of the talk: TBD

Abstract
Abstract: Abstract.
Bio
Bio: Himabindu (Hima) Lakkaraju is an assistant professor at Harvard University focusing on explainability, fairness, and robustness of machine learning models. She has also been working with various domain experts in policy and healthcare to understand the real-world implications of explainable and fair ML. Hima has been named as one of the world’s top innovators under 35 by both MIT Tech Review and Vanity Fair. Her research has also received best paper awards at SIAM International Conference on Data Mining (SDM) and INFORMS, and grants from NSF, Google, Amazon, and Bayer. Hima has given keynote talks at various top ML conferences and workshops including CIKM, ICML, NeurIPS, AAAI, and CVPR, and her research has also been showcased by popular media outlets including the New York Times, MIT Tech Review, TIME magazine, and Forbes. More recently, she co-founded the Trustworthy ML Initiative to enable easy access to resources on trustworthy ML and to build a community of researchers, practitioners working on the topic.
                                                                                                                                                                                               
Manisha Verma

Manisha Verma

Amazon
Title of the talk: TBD

Abstract
Abstract: Abstract.
Bio
Bio: Manisha Verma is a Scientist at Amazon, NYC. She completed her PhD from University College London. Some of her recent work has been published at conferences such as WWW, RecSys, CIKM, WSDM and SIGIR. Over the past few years, she has worked with researchers at Google, Microsoft and Yahoo on improving advertisements. She has served on the program committee for WWW’22, SIGIR’21, ECIR’21, NAACL’20, NeuroIR17, DSHCM’17,’18, LearnIR’18 and CIKM’21.
                                                                                                                                                                                               
Xia Ning

Xia Ning

Ohio State University
Title of the talk: TBD

Abstract
Abstract: Abstract.
Bio
Bio: Ning was trained as a Computer Scientist. Ning’s research is on Data Mining, Machine Learning and Big Data Analytics, and their applications in Chemical Informatics, Drug Development, Medical Informatics and Health Informatics. She develops efficient data mining and machine learning methodologies to facilitate rapid and targeted exploration over chemical and biological spaces, and effective computational algorithms (e.g., recommendation, information retrivial) to analyze medical and healthcare data (e.g., electronic medical records, pharmacovigilance data). Her Ph.D. thesis was on Recommender Systems. Her research is currently supported by NSF and NIH.
                                                                                                                                                                                               

Accepted Papers

Organizers

Mansi Mane

Mansi Mane
Walmart Global Tech

Bio
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

Djordje Gligorijevic
eBay

Bio
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

Dingxian Wang
Upwork

Bio
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.
Behzad Shahrasbi

Behzad Shahrasbi
Amazon

Bio
Bio: Behzad Shahrasbi is Manager of Applied Science at Amazon. His team leads the efforts to proactively detect and prevent catalog abuse and protect brands at scale. His career also includes significant tenures at Walmart, SmartDrive Systems, and Nokia. Behzad research interests span NLP, Mulitimodal Representations, Recommender Systems, and Privacy-Preserving ML. Post completeion of his PhD, Behzad has published in several journals and conferences, including ICML, IEEE Trans. on Big Data, ICCV, and ICASP.
Topojoy Biswas

Topojoy Biswas
Walmart Global Tech

Bio
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

Evren Korpeoglu
Walmart Global Tech

Bio
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.
Marios Savvides

Marios Savvides
CMU, UltronAI

Bio
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.

Program Committee

  • ABC (XYZ University)