Mobility data mining and user modeling
- Geo-location and trajectory mining; activity, behavior, and preference modeling.
- Traffic, demand, and travel-time prediction; mobility forecasting for decisions.
- Large-scale graph and spatiotemporal learning.
From Mobility Data Mining to Closed-Loop Agentic Systems
Exploring how mobility recommendations, human responses, and agent decisions shape shared transportation systems.
AMI’27 explores how mobility systems can learn and make decisions when recommendations change user behavior and the conditions for future decisions. Navigation, ride-hailing, and delivery platforms make this challenge concrete: a recommendation that works for one user may have different effects when many users follow it. Understanding these feedback effects requires connecting mobility data mining with recommendation, user modeling, and agentic decision making.
The workshop brings together researchers and practitioners working across these fields, from learning mobility patterns to deploying interactive services. It connects to WSDM’s interests in data mining, recommender systems, and user behavior on large-scale digital platforms. Contributions can address individual components or complete systems, provided they examine the implications for decisions or interaction.
We welcome methodological, empirical, systems, dataset, and position contributions across three themes.
Across these themes, we encourage location privacy, equitable access, robustness, user agency, and responsible experimentation.
We welcome researchers and practitioners in data mining, recommendation, user modeling, agentic and multi-agent systems, and mobility applications.
The proposed half-day, in-person workshop combines invited talks, contributed talks, posters, and a panel.
Invited speakers and panelists will be announced soon.
Speaker informationSubmission and notification deadlines: 23:59 Anywhere on Earth (AoE, UTC−12), following the WSDM 2027 workshop timeline.
For questions about submissions, the workshop or participation, please get in touch.