Electronic commerce is an intriguing application field for algorithmic techniques originating from artificial intelligence research, machine learning and optimization due to the great demands on efficiency, usability and ease of integration with existing software systems and infrastructure. Our research team at the Lucerne University of Applied Sciences and Arts - The Lucerne School of Information Technology tackles these challenges by providing efficent, scalable and specificially tailored solutions to our partners for online and offline recommendation systems, preference-based search, user profiling and data analysis in general. These pages aim to make some of our research on the algorithmic machinery visible to a broader audience by providing a testbed of online demonstrators. Please check them out and feel free to get in touch with us!
A demonstration of the skyline operator for preference-based catalogue search.Demo
Enter your search preferences and navigate through the skyline shown as a graph on a hyperbolic plane.Demo
Experiment with our SQL inspired database language and specify your own preference queries.Demo
A digital rummage table realized with weighted preferences and skyline-based similarity.Demo
Browse the catalog and get recommendations for similar products, calculated with skyline algorithms.Demo
Let a deep neural network, trained on 300’000 recipes, generate a unique cooking recipe.Demo