Workshop Schedule

User Understanding from BigData
December 12th 2020, Virtually
Workshop Chairs: Claire Ding, Wutao Wei, Ying Lu
Time (Est) Title Presenter/Author
11:30 – 11:40 am Opening Remarks Claire Ding, Wutao Wei, Ying Lu
11:40 – 12:10 pm S22202: Representation of Click-Stream DataSequences for Learning User Navigational Behavior by Using Embeddings Erdi Olmezogullari and Mehmet Aktas
12:10 - 12:20 pm Coffee Break
12:20 - 12:50 pm S22206: Understanding User Understanding: What do Developers Expect from a Cognitive Assistant? Glaucia Melo, Edith Law, Paulo Alencar, & Donald Cowan
12:50 – 1:20 pm S22207: On Variational Inference for User Modeling in Attribute-Driven Collaborative Filtering Venugopal Mani, Ramasubramanian Balasubramanian, Sushant Kumar, Abhinav Mathur & Kannan Achan
1:20 – 1:30 pm Closing Remarks

Scope of the Workshop

This accepted workshop is to gain insights on how big data methodologies can be enhancing user understanding in the world of technology. The accepted papers will be presented in the workshop and be included in the workshop proceeding of IEEE Big Data Conference 2020.

Call for Papers

The availability of massive amounts of data has driven significant progress in the field of AI, in particular, data driven methods to understand human behavior has been an emerging topic in social science and human studies. Most internet companies need to leverage user level data from different sources to understand how users interact with their products in various scenarios and contexts. Quantitative techniques would increase generalizability of research conclusions regarding user mental models to provide frameworks for user understanding. On the other hand, large scale data can be critical to customize approaches in gaining user traction, improve user experience and monetization for different user groups. We’ve seen tremendous applications in this space, including but not limited to recommendation, marketing, online experiments, to name just a few. Fundamental understanding also requires methods such as statistical sampling, data visualization, funnel analysis, experimental design, causal inference etc.

This workshop aims to provide a platform for researchers from related fields to exchange ideas on how to use data-driven technologies for better user understanding through data analytics & modeling, experimental design and user research. The workshop will focus on both theoretical and practical challenges. Furthermore, it will place particular emphasis on algorithmic approaches in the context of learning, optimization, decision making, fairness and data privacy that raise fundamental challenges for existing techniques.

Perspective and vision papers are also welcome. Finally, the workshop welcomes papers that describe the public release of privacy-preserving datasets that the community can use to solve fundamental technical problems of interest in user understanding.


The topics for the workshop including but not limited to:

Paper Submission

Please submit a full-length paper (up to 10 page IEEE 2-column format) through the online submission system.

Paper Submission Page

Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines (see link to “formatting instructions” below).

Formatting Instructions

8.5” x 11” (DOC, PDF)

LaTex Formatting Macros

Important dates

Program Chairs

Program Committee Members

Contact Information