Linus Dietz, M.Sc.
|Phone:||+49 89 289-18676|
|Address:||TUM Institut für Informatik|
Chair of Connected Mobility (I11)
|Consulting hours:||by arrangement|
|Times of absence:||Jan 28 -- Feb 1, 2019|
Linus is researcher and PhD student at the Chair of Connected Mobility at the Technical University of Munich (TUM). He holds a M.Sc. in Applied Computer Science from the University of Bamberg.
- Recommender Systems
- Data Analytics
- Software Engineering
- Open Source Software
I'm working on data-driven destination recommender systems. The general scenario is a global planning tool for independent travelers. Initially, I characterize destination regions around the globe along a set of tourism-related dimensions such as typical attractions and costs. In the second step I design algorithms to match the user's preferences and constraints to destinations within the respective query regions.
Current Papers (to appear)
- Roy, R. and Dietz, L. W.: Modeling Physiological Conditions for Proactive Tourist Recommendations, in 23rd International Workshop on Personalization and Recommendation on the Web and Beyond
- Herzog, D.; Dietz, L. W. & Wörndl, W. Augstein, M.; Herder, E. & Wörndl, W. (Eds.) Tourist Trip Recommendations – Foundations, State of the Art and Challenges. Personalized Human-Computer Interaction, DeGruyter Oldenbourg, 2018
Open thesis topics
Please read through the description and the requirements of the topic carefully if you are interested in one of the topics.
- Currently no topics available!
Book: Java by Comparison
by Simon Harrer, Jörg Lenhard, and Linus Dietz
Published by the Pragmatic Bookshelf
Available at the University Library or the bookstore of your convenience.
Improve your coding skills by comparing your code to that of expert programmers, so you can write code that’s clean, concise, and to the point: code that others will read with pleasure and reuse. Get hands-on advice to level up your coding style through small and understandable examples that compare flawed code to an improved solution. Discover handy tips and tricks, as well as common bugs an experienced Java programmer needs to know. Make your way from a Java novice to a master craftsman.
- Online Evaluations for Everyone: Mr. DLib's Living Lab for Scholarly Recommendations. Advances in Information Retrieval, Lecture Notes in Computer Science, Springer, 2019 more…
- Code Process Metrics in University Programming Education. 2nd Workshop on Innovative Software Engineering Education, 2019 more…
- Analyzing the Importance of JabRef Features from the User Perspective. Proceedings of the 11h Central European Workshop on Services and their Composition, 2019 more…
- Modeling Physiological Conditions for Proactive Tourist Recommendations. 23rd International Workshop on Personalization and Recommendation on the Web and Beyond, 2019 more…
- Identifying Travel Regions Using Location-Based Social Network Check-in Data. Frontiers in Big Data 2, 2019 more…
- Deriving Tourist Mobility Patterns from Check-in Data. Proceedings of the WSDM 2018 Workshop on Learning from User Interactions, 2018 more…
- Teaching Clean Code. Proceedings of the 1st Workshop on Innovative Software Engineering Education, 2018 more…
- Characterisation of Traveller Types Using Check-In Data from Location-Based Social Networks. In: Information and Communication Technologies in Tourism 2019. Springer, 2018 more…
- Java by Comparison – Become a Java Craftsman in 70 Examples. The Pragmatic Bookshelf, 2018 more…
- Data-Driven Destination Recommender Systems. Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, ACM, 2018 more…
- Recommending Crowdsourced Trips on wOndary. Proceedings of the RecSys Workshop on Recommenders in Tourism, 2018 more…
- Affective Computing and Bandits: Capturing Context in Cold Start Situations. Proceedings of the RecSys Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, 2018 more…
- Gesellschaft für Informatik (GI) e.V.
- Association for Computing Machinery (ACM)