Integrity in Social Networks and Media
Duration: All-Day
Summary: Integrity 2024 is the 5th edition of the Workshop on Integrity in Social Networks and Media. The goal of the workshop is to bring together academic and industry researchers working on integrity, fairness, trust and safety in social networks to discuss the most pressing risks and cutting-edge technologies to reliably measure and mitigate them. The themes for this year will include integrity risks and opportunities with generative AI and multimodal content understanding for integrity. The event will include invited talks from academic experts and industry leaders as well as peer-reviewed papers and posters through an open call-for-papers.
Organizers: Lluis Garcia-Pueyo (Meta), Symeon Papadopoulos (ITI-CERTH), Prathyusha Senthil Kumar (Meta), Aristides Gionis (KTH Royal Institute of Technology), Panayiotis Tsaparas (University of Ioannina), Vasilis Verroios (Meta), Giuseppe Manco (ICAR-CNR), Anton Andryeyev (Meta), Stefano Cresci (IIT-CNR), Timos Sellis (Archimedes / Athena Research Center), Anthony McCosker (Swinburne University)
WSDM 2024 Workshop on Large Language Models for Individuals, Groups, and Society
Duration: All-Day
Summary: This workshop discusses the cutting-edge developments in research and applications of personalizing large language models (LLMs)and adapting them to the demands of diverse user populations and societal needs. The full-day workshop plan includes several keynotes and invited talks, a poster session and a panel discussion.
Organizers: Michael Bendersky (Google Research), Cheng Li (Google Research), Qiaozhu Mei (University of Michigan), Vanessa Murdock (Amazon), Jie Tang (Tsinghua University), Hongning Wang (University of Virginia), Hamed Zamani (University of Massachusetts Amherst), Mingyang Zhang (Google Research)
Psychology-informed Information Access Systems
Duration: Half-Day
Summary: Information access systems, such as search, retrieval, and recommendation systems, are nowadays data-driven and rely heavily on advanced machine learning algorithms to model and predict user preferences, needs, or intents, which are influenced by manifold psychological factors of their users. In stark contrast, the field of psychology has a long-standing tradition of rigorous research to formulate comprehensive theories and models aimed at explaining the human mind and behavior.
The PsyIAS workshop bridges these disciplines, aiming to connect the research communities of information retrieval, recommender systems, and natural language processing with cognitive and behavioral psychology. It serves as a forum for exchanging ideas and engaging in multidisciplinary discussions about the use of psychological constructs, theories, and empirical findings for modeling and predicting user preferences, intents, and behaviors. In particular, research about incorporating such psychology-inspired models into the search, retrieval, and recommendation processes, and corresponding algorithms and systems, are in the focus of PsyIAS. On a more fundamental level, PsyIAS also embraces research that looks into the role of cognitive processes underlying human information access.
Organizers: Markus Schedl (Johannes Kepler University), Marta Moscati (Johannes Kepler University), Bruno Sguerra (Deezer Research), Romain Hennequin (Deezer Research), Elisabeth Lex (Graz University of Technology)
The 3rd Workshop on Interactive and Scalable Information Retrieval Methods for eCommerce (ISIR-eCom)
Duration: All-Day
Summary: Over the past few years, consumer behavior has shifted from traditional in-store shopping to online shopping. For example, eCommerce sales have grown from around 5% of total US sales in 2012 to around 15.4% in year 2023. This rapid growth of eCommerce has created new challenges and vital new requirements for intelligent information retrieval systems.
Scalable systems: Since the pandemic hit, eCommerce became an important part of people’s routine and they started using online shopping for smallest grocery items to big electronics as well as cars. With such a large assortment of products and millions of users, achieving higher scalability without losing accuracy is a leading concern for information retrieval systems for eCommerce.
Interactive Systems: The diverse buyers make the relevance of the results highly subjective, because relevance varies for different buyers. The most suitable and intuitive solution to this problem is to make the system interactive and provide correct relevance for different users. Hence, interactive information retrieval systems are becoming a necessity in eCommerce.
System improvement: To handle sudden change in buyers’ behavior, industries adopted existing sub-optimal in- formation retrieval techniques for various eCommerce tasks. Parallelly, they also started exploring/researching for better solutions and in dire need of help from research community
The objective of this workshop is to bring a diverse set of practitioners and researchers together and encourage them to share their ideas, challenges & solutions and research. This workshop will provide a forum to discuss and learn the latest trends for interactive and scalable information retrieval approaches for eCommerce.
This workshop will provide a forum to discuss and learn the latest trends for interactive and scalable information retrieval approaches for eCommerce. It will provide academic and industrial researchers a platform to present their latest works, share research ideas, present and discuss various challenges, and identify the areas where further research is needed. It will foster the development of a research community focused on solving eCommerce-related information retrieval problems that provide superior eCommerce experience to all users.
Organizers: Vachik S. Dave (Walmart Global Tech), Linsey Pang (Salesforce), Xiquan Cui (The Home Depot), Chen Luo (Amazon), Hamed Zamani (University of Massachusetts, Amherst), Lingfei Wu (Pinterest), George Karypis (University of Minnesota Twin-cities & Amazon)
The 5th International Workshop on Machine Learning on Graphs (MLoG)
Duration: All-Day
Summary: Graphs, which encode pairwise relations between entities, are a kind of universal data structure for a lot of real-world data, including social networks, transportation networks, and chemical molecules. Many important applications on these data can be treated as computational tasks on graphs. Recently, machine learning techniques are widely developed and utilized to effectively tame graphs for discovering actionable patterns and harnessing them for advancing various graph-related computational tasks. Huge success has been achieved and numerous real-world applications have benefited from it. However, since in today’s world we are generating and gathering data in a much faster and diverse way, real-world graphs are becoming increasingly large-scale and complex. More dedicated efforts are needed to propose more advanced machine learning techniques and properly deploy them for real-world applications in a scalable way.
MLoG at WSDM’24 provides a venue to gather the academia researchers and industry researchers/practitioners to present the recent progress of machine learning on graphs.
Organizers: Tyler Derr (Vanderbilt University), Yao Ma (Rensselaer Polytechnic Institute), Kaize Ding (Northwestern University), Tong Zhao (Snap Inc.), Nesreen K. Ahmed (Intel Research Labs)
Representation Learning and Clustering (RLC’24)
Duration: Half-Day
Summary: Data clustering and representation learning play an indispensable role in data science. They are very useful to explore massive data in many fields, including information retrieval, natural language processing, bioinformatics, recommender systems, and computer vision. Despite their success, most existing clustering methods are severely challenged by the data generated by modern applications, which are typically high dimensional, noisy, heterogeneous, and sparse or even collected from multiple sources or represented by multiple views where each describes a perspective of the data. This has driven many researchers to investigate new deep clustering models to overcome these difficulties. One promising category of such models relies on representation learning. Indeed, learning a good data representation is crucial for clustering algorithms, and combining the two tasks is a common way of exploring this type of data. The idea is to embed the original data into a low dimensional latent space and then perform clustering on this new space. Both tasks can be carried out sequentially or jointly; combining the two tasks is a common way of exploring this type of data.
Hence, one main goal of the workshop is to bring together the leading researchers who work on state-of-the-art deep unsupervised feature extraction and clustering models, and also the practitioners who seek novel applications. In summary, this workshop is an opportunity to:
- Present the recent advances in representation learning and clustering methods including multi-view clustering and semi-supervised learning which are not explored well.
- Outline potential applications that could inspire new deep approaches.
- Explore benchmark data to better evaluate and study deep clustering models.
- Evaluate the effectiveness of deep clustering models compared to classical approaches in terms of interpretability of clusters and Scalability.
The RLC workshop intends to promote research at the intersection of representation learning and clustering, and its application to real-life data science challenges. The workshop welcomes both high-quality academic (theoretical or empirical) and practical papers on unsupervised graph representation learning for clustering and related work.
Organizers: Lazhar Labiod (Université de Paris Cité), Aghiles Salah (Rakuten RIT), Mohamed Nadif (Université de Paris Cité)