Overview
Personalization is key in understanding user behavior and has been a main focus in the fields of knowledge discovery and information retrieval. Building personalized recommender systems is especially important now due to the vast amount of user-generated textual content, which offers deep insights into user preferences. The recent advancements in Large Language Models (LLMs) have significantly impacted research areas, mainly in Natural Language Processing and Knowledge Discovery, giving these models the ability to handle complex tasks and learn context.
However, the use of generative models and user-generated text for personalized systems and recommendation is relatively new and has shown some promising results. This workshop is designed to bridge the research gap in these fields and explore personalized applications and recommender systems. We aim to fully leverage generative models to develop AI systems that are not only accurate but also focused on meeting individual user needs. Building upon the momentum of previous successful forums, this workshop seeks to engage a diverse audience from academia and industry, fostering a dialogue that incorporates fresh insights from key stakeholders in the field.
Call for papers
We will welcome papers that leverage generative models with a goal of recommendation and personalization on several topics including but not limited to those mentioned in CFP. Papers can be submitted at OpenReview.
Information for the day of the workshop
Workshop at KDD2025
- Submission deadline: May 8th, 2025
- Author notifications: June 8th, 2025
- Meeting: August 6th, 2025
Schedule
| Time (EDT) | Agenda |
|---|---|
| 2:00-2:10pm | Opening remarks |
| 2:10-2:50pm | Keynote by Ed Chi (40 min) |
| 2:50-3:30pm | Coffee Break/Poster Session |
| 3:30-4:10pm | Keynote TBD (40 min) |
| 4:15-4:55pm | Keynote TBD (40 min) |
| 5:00-6:00pm | Panel Discussion (60 min) Panelists: Ed Chi, TBD |
Keynote Speakers
Ed Chi
Google DeepMind
Title TBD
Abstract: Abstract will be updated closer to the workshop date.
Bio
Bio: Ed H. Chi is a Distinguished Scientist at Google DeepMind, leading machine learning research teams working on large language models (from LaMDA leading to launching Bard/Gemini), and neural recommendation agents. With 39 patents and ~200 research articles, he is also known for research on user behavior in web and social media. As the Research Platform Lead, he helped launched Bard/Gemini, a conversational AI experiment, and delivered significant improvements for YouTube, News, Ads, Google Play Store at Google with >660 product improvements since 2013. Prior to Google, he was Area Manager and Principal Scientist at Xerox Palo Alto Research Center’s Augmented Social Cognition Group in researching how social computing systems help groups of people to remember, think and reason. Ed earned his 3 degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota. Inducted as an ACM Fellow and into the CHI Academy, he also received a 20-year Test of Time award for research in information visualization. He has been featured and quoted in the press, including the Economist, Time Magazine, LA Times, and the Associated Press. An avid golfer, swimmer, photographer and snowboarder in his spare time, he also has a blackbelt in Taekwondo.
Panelists
Ed Chi
Google DeepMind
Bio: Ed H. Chi is a Distinguished Scientist at Google DeepMind, leading machine learning research teams working on large language models (LaMDA/Bard), neural recommendations, and reliable machine learning. With 39 patents and ~200 research articles, he is also known for research on user behavior in web and social media. As the Research Platform Lead, he helped launched Bard, a conversational AI experiment, and delivered significant improvements for YouTube, News, Ads, Google Play Store at Google with >660 product improvements since 2013. Prior to Google, he was Area Manager and Principal Scientist at Xerox Palo Alto Research Center’s Augmented Social Cognition Group in researching how social computing systems help groups of people to remember, think and reason. Ed earned his 3 degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota. Inducted as an ACM Fellow and into the CHI Academy, he also received a 20-year Test of Time award for research in information visualization. He has been featured and quoted in the press, including the Economist, Time Magazine, LA Times, and the Associated Press. An avid golfer, swimmer, photographer and snowboarder in his spare time, he also has a blackbelt in Taekwondo
Accepted Papers
- Placeholder Paper for GenAIRecP 2025
Author One, Author Two, Author ThreeAbstractAbstract: This is a placeholder abstract for the GenAIRecP 2025 workshop. Actual papers will be added after the submission and review process is complete.PDF Code
Organizers
Narges Tabari
AWS AI Labs
Bio: Narges Tabari is an Applied Scientist working at AWS AI Labs. She received her PhD in 2018 in Computer Science at the University of North Carolina. She mainly wroks towards applications of NLP, from sentiment analysis, emotion detection, summarization, text generation, and intersection of NLP with recommender systems and personalization. Before joining Amazon, she was a Research Scientist at the University of Virginia and an NLP Engineer at Genentech. She has served as Session Chair for NAACL 2022 Industry Track, and has extensive experience reviewing for conferences such as NAACL, AAAI, and ACL.
Aniket Deshmukh
AWS AI Labs
Bio: Aniket is an Applied Scientist at AWS AI Labs, focusing on recommendation systems and large language models. Previously, as a Senior Applied Scientist at Microsoft AI and Research, he contributed to Microsoft Advertising by working on multimedia ads, smart campaigns, and auto-bidding projects. Aniket earned his PhD in Electrical and Computer Engineering from the University of Michigan, Ann Arbor, focusing on domain generalization and reinforcement learning. He is an active contributor to the academic community, regularly reviewing for conferences such as NeurIPS, ICML, CVPR, AISTATS, and JMLR, and has been recognized as a top reviewer at NeurIPS in 2021 and 2023, as well as AISTATS in 2022. Aniket has experience in organizing workshops at conferences like ICLR and WWW.
Wang-Cheng Kang
Google DeepMind
Bio: Dr. Wang-Cheng Kang is a Staff Research Engineer at Google DeepMind, working on LLM/GenAI for RecSys and LLM data efficiency. He held a PhD in Computer Science from UC San Diego, and interned at Adobe Research, Pinterest Labs, and Google Brain, focusing on recommender systems. He received RecSys’17 Best Paper Runner-up, and proposed SASRec, the first Transformer-based recommendation method.
Neil Shah
Snap Research
Bio: Dr. Neil Shah is a Principal Scientist at Snapchat. His research focuses on large-scale user representation learning, recommender systems and efficient ML. His work has resulted in 70+ refereed publications at top data mining and machine learning venues. He has also served as an organizer across multiple venues including KDD, WSDM, SDM, ICWSM, ASONAM and more, and received multiple best paper awards (KDD, CHI), departmental rising star awards (NCSU), and outstanding service and reviewer awards (NeurIPS, WSDM). He has also served as an organizer across multiple workshops and tutorials at KDD, AAAI, ICDM, CIKM and more.
Julian McAuley
University of California, San Diego
Bio: Julian McAuley has been a professor in the Computer Science Department at the University of California, San Diego since 2014. Previously he was a postdoctoral scholar at Stanford University after receiving his PhD from the Australian National University in 2011. His research is concerned with developing predictive models of human behavior using large volumes of online activity data. He has organized a large number of workshops, including workshops on recommendation, e-commerce, and natural language processing.
James Caverlee
Texas A&M University
Bio: James Caverlee received his Ph.D. in Computer Science from Georgia Tech in 2007, co-advised by Ling Liu (CS) and Bill Rouse (ISYE). Before that, he earned two M.S. degrees from Stanford University: one in Computer Science in 2001 and one in Engineering-Economic Systems & Operations Research in 2000. His undergraduate degree is a B.A. in Economics (magna cum laude) from Duke University in 1996. James Caverlee joined the faculty at Texas A&M in 2007. He spent most of his sabbatical in 2015 at Google as a Visiting Scientist in Ed Chi’s group. He has been honored to receive an NSF CAREER award, DARPA Young Faculty award, a AFOSR Young Investigator award, as well as several teaching awards.
George Karypis
University of Minnesota
Bio: Dr. George Karypis is a Senior Principal Scientist at AWS AI and a Distinguished McKnight University Professor and William Norris Chair in Large Scale Computing at the Department of Computer Science & Engineering at the University of Minnesota. His research interests span the areas of data mining, machine learning, high performance computing, information retrieval, collaborative filtering, bioinformatics, cheminformatics, and scientific computing. His research has resulted in the development of software libraries for serial and parallel graph partitioning (METIS and ParMETIS), hypergraph partitioning (hMETIS), for parallel Cholesky factorization (PSPASES), for collaborative filtering-based recommendation algorithms (SUGGEST), clustering high dimensional datasets (CLUTO), finding frequent patterns in diverse datasets (PAFI), and for protein secondary structure prediction (YASSPP). He has coauthored over 350 papers on these topics and two books (“Introduction to Protein Structure Prediction: Methods and Algorithms” (Wiley, 2010) and “Introduction to Parallel Computing” (Publ. Addison Wesley, 2003, 2nd edition)). He is serving on the program committees of many conferences and workshops on these topics, and on the editorial boards of the IEEE Transactions on Knowledge and Data Engineering, ACM Transactions on Knowledge Discovery from Data, Data Mining and Knowledge Discovery, Social Network Analysis and Data Mining Journal, International Journal of Data Mining and Bioinformatics, the journal on Current Proteomics, Advances in Bioinformatics, and Biomedicine and Biotechnology. He is a Fellow of the IEEE.