Recommender system, World Wide Web, Variety, Artificial intelligence and Data science are his primary areas of study. Many of his research projects under Recommender system are closely connected to Point with Point, tying the diverse disciplines of science together. His World Wide Web research is multidisciplinary, relying on both User interface and Adaptation.
In his research on the topic of Variety, Transparency is strongly related with Knowledge management. His Data science research integrates issues from Software system, Expert system and Taxonomy. As a member of one scientific family, he mostly works in the field of Information retrieval, focusing on Service and, on occasion, Data mining.
His primary areas of study are Recommender system, World Wide Web, Artificial intelligence, Information retrieval and Personalization. The concepts of his Recommender system study are interwoven with issues in Variety, Session, Process and Data science. Dietmar Jannach has included themes like Quality, User interface and Software engineering in his Process study.
His work on Artificial intelligence is being expanded to include thematically relevant topics such as Machine learning. Information retrieval connects with themes related to Data mining in his study. Dietmar Jannach combines subjects such as Task and Human–computer interaction with his study of Personalization.
His primary areas of investigation include Recommender system, Artificial intelligence, Data science, Deep learning and Session. His Recommender system research includes elements of Value, Preference and Personalization. His Artificial intelligence study integrates concerns from other disciplines, such as Quality and Machine learning.
His Data science research integrates issues from Ranking, User experience design and Information system. His Deep learning study combines topics in areas such as Field and Heuristic. His Session research is multidisciplinary, incorporating perspectives in Class and Human–computer interaction.
Recommender system, Artificial intelligence, Deep learning, Data science and Session are his primary areas of study. His Recommender system research includes themes of Ranking, Information overload and Personalization. The study incorporates disciplines such as Class, Machine learning, User-centered design and Multimedia in addition to Artificial intelligence.
Dietmar Jannach has researched Machine learning in several fields, including Variety and Graph. His Deep learning research incorporates themes from Radio broadcasting, Feature and Heuristic. His studies deal with areas such as Quality, Recurrent neural network and Information retrieval, Relevance as well as Session.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Recommender Systems: An Introduction
Dietmar Jannach;Markus Zanker;Alexander Felfernig;Gerhard Friedrich.
(2010)
Beyond accuracy: evaluating recommender systems by coverage and serendipity
Mouzhi Ge;Carla Delgado-Battenfeld;Dietmar Jannach.
conference on recommender systems (2010)
Recommender Systems: RECENT DEVELOPMENTS
Dietmar Jannach;Markus Zanker;Alexander Felfernig;Gerhard Friedrich.
(2010)
Conceptual modeling for configuration of mass-customizable products
Alexander Felfernig;Gerhard Friedrich;Dietmar Jannach.
Artificial Intelligence in Engineering (2001)
Consistency-based diagnosis of configuration knowledge bases
Alexander Felfernig;Gerhard Friedrich;Dietmar Jannach;Markus Stumptner.
Artificial Intelligence (2004)
UML AS DOMAIN SPECIFIC LANGUAGE FOR THE CONSTRUCTION OF KNOWLEDGE-BASED CONFIGURATION SYSTEMS
Alexander Felfernig;Gerhard E. Friedrich;Dietmar Jannach.
International Journal of Software Engineering and Knowledge Engineering (2000)
An Integrated Environment for the Development of Knowledge-Based Recommender Applications
Alexander Felfernig;Gerhard Friedrich;Dietmar Jannach;Markus Zanker.
International Journal of Electronic Commerce (2006)
When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation
Dietmar Jannach;Malte Ludewig.
conference on recommender systems (2017)
How should I explain? A comparison of different explanation types for recommender systems
Fatih Gedikli;Dietmar Jannach;Mouzhi Ge.
International Journal of Human-computer Studies / International Journal of Man-machine Studies (2014)
Automated Generation of Music Playlists: Survey and Experiments
Geoffray Bonnin;Dietmar Jannach.
ACM Computing Surveys (2014)
Profile was last updated on December 6th, 2021.
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