4th Crowd Science Workshop — CANDLE: Collaboration of Humans and Learning Algorithms for Data Labeling
Duration: Half-Day (PM)
Summary: Crowdsourcing has been used to produce impactful and large-scale datasets for Machine Learning and Artificial Intelligence (AI), such as ImageNET, SuperGLUE, etc. Since the rise of crowdsourcing in early 2000s, the AI community has been studying its computational, system design, and data-centric aspects at various angles at such workshops as CSS, CrowdML, DCAI, and HILL. We welcome the studies on developing and enhancing of crowdworker-centric tools, that offer task matching, requester assessment, instruction validation, among other topics. We are also interested in exploring methods that leverage the integration of crowdworkers to improve the recognition and performance of the machine learning models. Thus, we invite studies that focus on shipping active learning techniques, methods for joint learning from noisy data and from crowds, novel approaches for crowd-computer interaction, repetitive task automation, and role separation between humans and machines. Moreover, we invite works on designing and applying such techniques in various domains, including e-commerce and medicine.
Organizers: Dmitry Ustalov (Toloka), Saiph Savage (Northeastern University), Niels van Berkel (University of Aalborg) and Yang Liu (UCSC)
Summary: This is the proposal for the fourth edition of the Workshop on Integrity in Social Networks and Media, Integrity 2023, following the success of the first three Workshops held in conjunction with the 13th, 14th, and 15th ACM Conference on Web Search and Data Mining (WSDM) in 2020 , 2021 , and 2022 . The goal of the workshop is to bring together researchers and practitioners to discuss content and interaction integrity challenges in social networks and social media platforms. The event consists of (1) a series of invited talks by reputed members of the Integrity community from both academia and industry, (2) a call-for-papers for contributed talks and posters, and (3) a panel with the speakers.
Organizers: Luis Garcia Pueyo (Meta), Panayiotis Tsaparas (University of Ioannina), Prathyusha Senthil Kumar (Meta), Timos Sellis (Archimedes / Athena Research Center), Paolo Papotti (EURECOM), Sibel Adali (Rensselaer Polytechnic Institute), Giuseppe Manco (ICAR-CNR), Tudor Trufinescu (Meta), Gireeja Ranade (UC Berkeley), James Verbus (LinkedIn), Mehmet N Tek (Google) and Anthony McCosker (Swinburne Social Innovation Research Institute)
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. Thus, we propose MLoG as a full day workshop at WSDM’23, which provides a venue to gather academia researchers and industry researchers/practitioners to present the recent progress of machine learning on graphs.
Organizers: Tyler Derr (Vanderbilt University), Yao Ma (New Jersey Institute of Technology), Benedek Rozemberczki (Isomorphic Labs), Neil Shah (Snap Inc.) and Shirui Pan (Griffith University)
Duration: Half-Day (AM)
Summary: A knowledge graph (KG) consists of numerous triples, in which each triple, i.e., (head entity, relation, tail entity), denotes a real-world assertion. Many large-scale KGs have been developed, e.g., general-purpose KGs Freebase and YAGO. Also, lots of domain-specific KGs are emerging, e.g., COVID-19 KGs, biomedical KGs, and agricultural KGs. By embedding KGs into low-dimensional vectors, i.e., representations of entities and relations, we could integrate KGs into machine learning models and enhance the performance of many prediction tasks, including search, recommendations, and question answering.
During the construction, refinement, embedding, and application of KGs, a variety of KG learning algorithms have been developed to handle challenges in various real-world scenarios. This workshop aims to engage with active researchers from KG communities, recommendation communities, natural language processing communities, and other communities, and deliver state-of-the-art research insights into the core challenges in KG learning. Graph neural networks have also brought new opportunities to KG learning. We are looking for novel algorithms, theories, and applications related to knowledge graph learning. Potential topics include but are not limited to KG-enhanced web search, KG-based recommender systems, KG-based virtual assistant systems, effective KG construction algorithms dedicated to specific scenarios, efficient graph neural networks tailored for KG embedding, efficient contrastive learning models tailored for KG refinement, including KG completion and KG error detection, theoretical analysis of and insights into KG reasoning, as well as algorithms that bridge the gap between KG embedding and applications in various domains, such as recommendations, education, sports, and transportation.
Organizers: Qing Li (The Hong Kong Polytechnic University), Xiao Huang (The Hong Kong Polytechnic University), Ninghao Liu (University of Georgia), Yuxiao Dong (Tsinghua University) and Guansong Pang (Singapore Management University)
Duration: Half-Day (PM)
Summary: Interactive recommender systems have attracted increasingly research attentions from both academia and industry. The proposed workshop is a half-day event, which provides a forum for researchers and practitioners to discuss recent research progress and novel research directions about interactive recommender systems. The program will include two keynotes and 6∼8 research paper presentations. The objective of this workshop is to consolidate the recent progress in interactive recommendation techniques, which will be a promising research and development direction for future recommendation technologies. Moreover, this workshop also aligns with WSDM’ spirit of promoting the collaborations between academia and industry.
Organizers: Yong Liu (Huawei Noah’s Ark Lab), Hao Zhang (Huawei Noah’s Ark Lab), Zhu Sun (Institute of High Performance Computing, A*STAR), Shoujin Wang (University of Technology Sydney Australia) and Jie Zhang (Nanyang Technological University)