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
Deadline extended to 10 June 2024. 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.
Call for reviewers
We are also looking for reviewers with relevant experience. Please fill this form if you are interested.
Information for the day of the workshop
Workshop at KDD2024
- Submission deadline:
28 May 202410 June 2024 - Author notifications: 28 June 2024
- Meeting: 25/26 August 2024
Schedule
We have a half-day program either from 8am to 12pm or 1pm to 5pm on Monday (Aug. 26) at Location: TBD.
| Time (PDT) | Agenda |
|---|---|
| 8:00-8:10am | Opening remarks |
| 8:10-8:50am | Keynote by TBD TBD (40 min): XYZ |
| 8:50-9:30am | Keynote by TBD TBD (40 min): XYZ |
| 9:30-10:30am | Coffee Break/Poster Session |
| 10:30-11:00am | Keynote by TBD TBD (30 min): XYZ |
| 11:00-12:00pm | Panel Discussion (60 min) Moderator:TBD Panelists: TBD, TBD, TBD |
| 12:00-12:10pm | Closing Remarks |
Keynote Speakers
Ed Chi
Google DeepMind
Title of the talk: TBD
Abstract: Abstract.
Bio
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
Dietmar Jannach
University of Klagenfurt, University of Bergen
Title of the talk: TBD
Abstract: Abstract.
Bio
Bio: Dietmar Jannach is a Professor at the University of Klagenfurt in Austria. He has authored more than 150 publications in areas including recommender systems technology, knowledge-based systems development, constraint-based systems, semantic web applications and web mining, and software engineering. Among his publications, Jannach is a co-author of the book Recommender Systems: An Introduction. His current line of research is focused on the design and evaluation of machine learning algorithms for recommender systems and on the impact and value of recommender systems in practice.
Xiao-Ming Wu
Xiao-Ming Wu
Title of the talk: TBD
Abstract: Abstract.
Bio
Bio: Dr Wu received her PhD in Electrical Engineering from Columbia University with her dissertation titled “Learning on Graphs with Partially Absorbing Random Walks: Theory and Practice”. She received her bachelor degree in Applied Mathematics and master degree in Computer Science, both from Peking University, and her MPhil degree in Information Engineering from the Chinese University of Hong Kong. Her research interests are in the broad areas of machine learning, pattern recognition, and data mining, with a particular focus on graph algorithms and their applications. She has strong interests in both fundamental and applied research, and regularly publishes in prestigious venues such as NIPS and CVPR. Her thesis research has contributed to theoretical understanding of state-of-the-art methods and improved upon them significantly for various applications. Her approaches have been currently adopted in industry.
Dawen Liang
Netflix
Title of the talk: TBD
Abstract: Abstract.
Bio
Bio: Dawen Liang is a research scientist at Netflix, working on core personalization algorithms. His research interests include probabilistic models and (approximate) inference, causal inference, reinforcement learning, and their applications to recommender systems. He completed his Ph.D. in the Electrical Engineering Department at Columbia University, working on probabilistic latent variable models for analyzing music, speech, text, and user behavior data.
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
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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.
Rashmi Gangadharaiah
AWS AI Labs
Bio: Dr. Rashmi Gangadharaiah is a Principal Machine Learning scientist in AWS AI, Amazon. She currently works in the area of Conversational AI, focused on task-oriented dialog systems. She has previously worked on applications in the areas of healthcare analytics, question answering, information retrieval - especially from social media, machine translation and speech science. She was previously a Research Staff Member at IBM Research where she worked on knowledge discovery, drug safety and interactive dialog systems in customer support settings. She was also a postdoctoral scholar at UCSD where she worked with several infectious disease doctors to build an interactive decision support system for differential diagnosis. Dr. Gangadharaiah earned her PhD in information technology, artificial intelligence, and machine learning from Carnegie Mellon University. She has experience organizing workshops (NLP4MC at ACL’20, NLP4MC at NAACL’21) and Industry Tracks (NAACL’22 Industry Track Chair) at top-tier NLP/ML conferences.
Hamed Zamani
University of Massachusetts Amherst
Bio: Hamed Zamani is an Assistant Professor at the University of Massachusetts Amherst, where he also serves as the Associate Director of the Center for Intelligent Information Retrieval (CIIR), one of the top academic research labs in Information Retrieval worldwide. Prior to UMass, he was a Researcher at Microsoft working on search and recommendation problems. His research focuses on designing and evaluating (interactive) information access systems, including search engines, recommender systems, and question answering. His work has led to over 85 refereed publications in the field and has led to pioneering work on LLM personalization. He is a recipient of the NSF CAREER Award, ACM SIGIR Early Career Excellence in Research and Community Engagement awards, and Amazon Research Award. He is an Associate Editor of the ACM Transactions on Information Systems (TOIS), has organized multiple workshops at the SIGIR, RecSys, WSDM, and WWW conferences, and served as a PC Chair at SIGIR 2022 (Short Papers)
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.
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.
Program Committee
- ABC (XYZ University)