Interacting with Adaptive Services and Recommender Systems

We investigate selected research problems regarding user modeling and personalization, especially interactive recommender systems. Delivering accurate and timely information is particularly valuable in mobile scenarios. Therefore it is desirable to assist the user by services that are tailored towards her context. The main goal is to improve the user experience when interacting with mobile recommender systems and other adaptive services.

General topic areas: recommender systems, user modeling, human-computer interaction and mobility.

Dr. Wolfgang Wörndl  

Proactive and Context-aware Recommender Systems

  • Proactivity: system pushes recommendations to user when current situation seems appropriate
  • Model for proactivity in recommender systems
  • Study user interface issues
  • Application in various domains, e.g. in-car recommendation or personal information management

Proactivity model 

User Interaction with Recommender Systems on Mobile Devices

  • Guidelines for user interfaces of mobile recommenders
  • Investigating the effect of interaction methods on users’ rating behavior
  • Conversational and critique-based interaction with exploratory recommender systems on mobile devices
  • Distributed user interfaces, e.g. with smartwatches

Mobile interaction

Combining Multiple Items in Travel Recommendation


Travel Recommender

Mobile User Modeling and Adaptation

  • Learning adaptation rules on mobile devices based on user behavior and context
  • Framework for mobile user activity logging
  • Adaptive information access and intelligent user interfaces, privacy issues

 Mobile user modeling


Collaborative Applications and Group Recommendation

  • Distributed UI and consensus building for group recommender
  • Example: rate items on private device, display results on shared tabletop screen
  • Social and collaborative mobility services

 Distributed UI