Related funded research projects
- KOLEGEA++ – Adaptive recommendations to support cooperative learning
- SoFar – Investigating methods to automatically improve dialogue-based product recommenders
- colognE-mobil II – Exploring the multifarious facets of e-mobility, with a focus on smart navigation solutions
- CONTici – Context-adaptive interaction in cooperative knowledge processes
- WISE – Novel approaches for Web Information and Service Engineering
- Movie Landscape – A 3D interface, that helps expressing personal preferences in large information spaces
- TagMF – Tag-enhanced Matrix Factorization for increasing transparency and interactive control
- Hootle – A hotel recommender system for groups that supports preference negotiation
- MyMovieMixer – Integrating interactive information filtering and algorithmic recommender techniques
- Hybreed Framework – Programming toolkit for complex, context-aware recommenders
Intelligent Interactive Systems
This research is guided by the vision of closely coupling user interaction with machine learning and reasoning techniques while keeping the user in control. Several current projects aim at improving recommender systems for electronic business and knowledge work. We have developed systems and software frameworks which focus among others on rapidly integrating hybrid recommenders, producing context-aware recommendations for individuals and groups, as well as giving the user more influence on the recommendations process. Besides, adaptivity of UIs is also particularly explored in the area of automotive systems and navigation solutions.
Recommender systems have become a widely used and effective means for personalizing information access and presentation in e-commerce and other application areas. Typically, a recommender system attempts to identify a subset of items from a very large information space that meet a user’s interests and preferences best among all alternatives, and subsequently presents those items to the user in a suitable manner. We are particularly interested in user-related aspects of such systems, e.g. increasing interactivity and improving transparency.
Context-adaptivity can be considered as a set of methods and techniques that is intended to enable users to perform their tasks with less effort or better results by taking the user’s current context into account. Depending on the respective use case, context can be virtually anything that is supposed to have an influence on the task at hand, for instance location, season, temperature, mood, or company of other people.
Smart Mobility Solutions
Navigation systems are widespread tools in automobiles while mobile applications assist smartphone users in other everyday life situations. Present solutions aim at optimizing routes without taking the user’s knowledge, experience, and preferences into account. Thus, we are investigating techniques for improving presentation of routes by incorporating personal user information and other data. Besides, we are exploring smart solutions to support intermodal mobility, i.e. using different modes of transport.
- A 3D Item Space Visualization for Presenting and Manipulating User Preferences in Collaborative Filtering. Kunkel, J., Loepp, B., and Ziegler, J. (to appear). IUI ’17: Proceedings of the 22th International Conference on Intelligent User Interfaces. New York, NY, USA: ACM. [BibTeX]
- Tag-Enhanced Collaborative Filtering for Increasing Transparency and Interactive Control. Donkers, T., Loepp, B., and Ziegler, J. (2016). Proceedings of the 24th Conference on User Modeling Adaptation and Personalization (UMAP ’16), 169–173. New York, NY, USA: ACM. [BibTeX] [DuEPublico] [Web]
- Supporting users in setting effective goals in activity tracking. Herrmanny, K., Ziegler, J., Dogangün, A., and PERSUASIVE 2016 (2016). Persuasive Technology: 11th International Conference ; PERSUASIVE 2016 ; Salzburg, Austria, April 5-7, 2016 ; Proceedings, 9638, 15–26. Cham: Springer International Publishing. [BibTeX] [DuEPublico]
- Blended Recommending: Integrating Interactive Information Filtering and Algorithmic Recommender Techniques. Loepp, B., Herrmanny, K., and Ziegler, J. (2015). Proceedings of the 33rd International Conference on Human Factors in Computing Systems (CHI ’15). New York, NY, USA: ACM. [BibTeX] [DuEPublico] [Web]
- Choice-based preference elicitation for collaborative filtering recommender systems. Loepp, B., Hussein, T., and Ziegler, J. (2014). Proceedings of the 32nd International Conference on Human Factors in Computing Systems (CHI ’14). New York, NY, USA: ACM. [BibTeX] [DuEPublico] [Web]
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