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

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

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.

Contact

Werner Gaulke

Researcher

Benedikt Loepp

Researcher

Johannes Kunkel

Researcher

Jesús Álvarez

Researcher

Kaveh Bakhtiyari

External PhD Student

Catalin-Mihai Barbu

Researcher

Helma Torkamaan

Researcher

Sidra Naveed

External PhD Student

Timm Kleemann

Researcher

Tim Donkers

Researcher

Related publications

Sequential User-based Recurrent Neural Network Recommendations

Donkers, T., Loepp, B., and Ziegler, J. (2017). Proceedings of the Eleventh ACM Conference on Recommender Systems, 152–160. New York: ACM.

A 3D Item Space Visualization for Presenting and Manipulating User Preferences in Collaborative Filtering

Kunkel, J., Loepp, B., and Ziegler, J. (2017). Proceedings of the 22nd International Conference on Intelligent User Interfaces, 3–15. New York, NY, USA: ACM.

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.

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.

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.

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.

Hybreed: A Software Framework for Developing Context-Aware Hybrid Recommender Systems

Hussein, T., Linder, T., Gaulke, W., and Ziegler, J. (2012). User modeling and user adapted interaction.

Generating Route Instructions with Varying Levels of Detail

Ziegler, J., Hussein, T., Münter, D., Hofmann, J., and Linder, T. (2011). 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI 2011).

Situationsgerechtes Recommending – Kontextadaptive, hybride Empfehlungsgenerierung

Hussein, T. and Ziegler, J. (2011). Informatik Spektrum, 34(2), 143–152.

Wahrnehmung und Akzeptanz von systemgenerierten Produktempfehlungen

Hussein, T., Gaulke, W., Hartmann, A., and Ziegler, J. (2010). Mensch & Computer 2010. München: Oldenbourg.

VIEW ALL RELATED PUBLICATIONS »

Related 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

colognE-mobil

Exploring the multifarious facets of electromobility

Related developments

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

Blended Recommending

Integrating interactive information filtering and algorithmic recommender techniques

Hybreed Framework

Programming toolkit for complex, context-aware recommenders