The following workshops have been selected for WSDM 2019, and are now seeking contributions.
Please go to their home pages via the links provided to find submission requirements and timelines, and further information about topic areas.
Task Intelligence Workshop
- Ahmed Hassan Awadallah, Microsoft Research, Redmond, US
- Cathal Gurrin, Dublin City University, Dublin, Ireland
- Mark Sanderson, RMIT University, Melbourne, Australia
- Ryen W. White, Microsoft Research, Redmond, US
Workshop Website: https://aka.ms/TaskIntelligence
Tasks are defined pieces of work that range in scope from specific (sending an email) to broad (planning a wedding), and are central to all aspects of information access and use. Task intelligence spans technologies and experiences to extract, understand, and support the completion of short- and long-term tasks. Helping users complete tasks is a key capability of search systems, digital assistants, and productivity applications and poses core challenges in data mining and knowledge representation and draws on additional research from areas such as machine learning and natural language processing. The workshop will comprise a mixture of: research paper presentations, reports from data challenge participants, including system demonstrations if available.
The International Workshop on Web-based Information Analysis towards Smart City (WISC’19) - WORKSHOP CANCELLED
- Professor Timos Sellis, Swinburne University of Technology, Hawthorn, Australia
- Professor Weiyi Meng, Binghamton University, NY, USA
- Dr. Flora Salim, RMIT University, Melbourne, Australia
- Dr. Ke Deng, RMIT University, Melbourne, Australia
Workshop Website: https://sites.google.com/view/wsdm19wisc/home
Web-based information sources such as online traffic information systems, online news, online sensors, online location-based services, and online social media provide dynamic information of a city. However, to advance towards smart city, there are essential tasks to be solved, including acquisition, integration, and analysis of big and heterogeneous data generated by a diversity of sources in urban spaces. The targeted audiences of the workshop are data science researchers and practitioners from academia, industry and government who have special interest in smart city, urban computing, location-based services, location-based community, geography & location data analysis, social networks and other related topics.
The 1st International Workshop on Context-aware Recommendation Systems with Big Data Analytics (CARS-BDA)
- Xiangmin Zhou, RMIT University, Australia
- Ji Zhang, University of Southern Queensland, Australia
- Yanchun Zhang, Victoria University, Australia
Workshop Website: http://wise-conferences.org/CARS-BDA/CARS-BDA.html
Effective recommendation of items of interest to consumers has become critical for enterprises in domains such as retail, e-commerce and online media. This workshop aims to bring together researchers with wide-ranging backgrounds to identify important research questions, to exchange ideas from different research disciplines, and, more generally, to facilitate discussion and innovation in the area of context-aware recommender systems and big data analytics.
DAPA: The WSDM 2019 Workshop on Deep Matching in Practical Applications
- Yixing Fan, Institute of Computing Technology, CAS ,Beijing, China
- Qingyao Ai, CICS, UMass Amherst, Amherst, MA, USA
- Zhaochun Ren, JD.com, Beijing, China
- Dawei Yin, JD.com , Beijing, China
- Jiafeng Guo, Institute of Computing Technology, CAS, Beijing, China
Workshop Website: https://wsdm2019-dapa.github.io/
Matching between two information objects is the core of many different information retrieval (IR) applications including Web search, question answering, and recommendation. Recently, deep learning methods have yielded immense success in speech recognition, computer vision, and natural language processing, significantly advancing state-of-the-art of
these areas. In the IR community, deep learning has also attracted much attention, and researchers have proposed a large number of deep matching models to tackle the matching problem for different IR applications. Despite the fact that deep matching models have gained significant progress in these areas, there are still many challenges to be addressed
when applying these models to real IR scenarios. In this workshop, we focus on the applicability of deep matching models to practical applications. We aim to discuss the issues of applying deep matching models to production systems, as well as to shed some light on the fundamental characteristics of different matching tasks in IR.
The WSDM 2019 Workshop on Interactive Data Mining
- Alan Said, University of Skövde, Sweden
- Denis Parra, PUC, Chile
- Juhee Bae, University of Skövde, Sweden
- Sepideh Pashami, Halmstad University, Sweden
Workshop Website: https://idatamining.github.io/2019/
In the age of Big Data, Artificial Intelligence, and Data Mining, an aspect often overlooked is the interactive and visual usability of frameworks, tools, and concepts used for ingesting and analyzing large quantities of data. This workshop focuses on aspects related to exactly this, i.e. how do we improve the interaction and usability of modern data mining approaches and how do make them accessible, understandable, and useful to non-experts. Taking an agnostic view of the application scenario, the workshop intends to serve as a forum for researchers and practitioners working at all levels of abstraction with data mining technologies. Interaction and interactive in the context of the workshop should be seen as anything from UI/UX-related aspects of visual interfaces, to more hands-on interaction with software and hardware used in the general areas of data mining, machine learning, and other concepts related broadly to artificial intelligence. The aim of this workshop is to explore existing and new interactive methods in machine learning and data mining that help the users to take better, and more informed, decisions. The primary audience of the workshop are researchers and practitioners in data mining from academia and industry with an interest in interaction with and interactivity of data mining approaches.