|Phone:||+49 89 289-18676|
|Address:||TUM Institut für Informatik |
Chair of Connected Mobility (I11)
|Consulting hours:||by arrangement|
|Times of absence:|
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.
I'm working together with Dr. Wolfgang Wörndl.
Please read through the description and the requirements of the topic carefully if you are interested in one of the topics.
Recommended Duration of Stay
Previously, we have done extensive research on traveler types based on mobility patterns and check-in behavior. One open research question for recommendation of a single destination as well as composite trips is the question of the recommended duration of stay. The task of this project would be to resolve the problem of how much of an item should be recommended to a user. A starting point could be the previously published RecSys late-breaking results paper "How Long to Stay Where? On the Amount of Item Consumption in Travel Recommendation" for the domain of destination recommendation; however, the scope of the analysis could be extended to other domains as well.
Dietz, L. W. & Woerndl, W.: How Long to Stay Where? On the Amount of Item Consumption in Travel Recommendation. ACM RecSys 2019 Late-breaking Results, 2019, 31-35
Dietz, L. W.; Sen, A.; Roy, R. & Woerndl, W.: Mining Trips from Location-Based Social Networks for Clustering Travelers and Destinations. Information Technology & Tourism, 2020, 22, 131-166
Choudhury, M. D.; Feldman, M.; Amer-Yahia, S.; Golbandi, N.; Lempel, R. & Yu, C.: Automatic Construction of Travel Itineraries Using Social Breadcrumbs. 21st ACM Conference on Hypertext and Hypermedia, ACM, 2010, 35-44
Xie, M.; Lakshmanan, L. V. S. & Wood, P. T.: Composite recommendations: from items to packages. Frontiers of Computer Science, 2012, 6, 264-277
Qualitative Mapping of Touristic Activities to Destinations
The idea of this project is to find out which activities are typically conducted in a touristic destination, such as city our countryside and then to visualize it in a interactive way. Information sources could be travel blogs, or mapping data, such as OpenStreetMaps. The challenge then is to decide which activities should be shown at which zoom-level of the map.
Sameera Thimbiri Palage: Travel Blog Articles for Qualitative Mapping of Touristic Activities to Destinations. TUM seminar thesis, 2019
Huang, Y. & Bian, L.: A Bayesian network and analytic hierarchy process based personalized recommendations for tourist attractions over the Internet. Expert Systems with Applications, Elsevier, 2009, 36, 933-943
McKenzie, G. & Adams, B.: In Clementini, E.; Donnelly, M.; Yuan, M.; Kray, C.; Fogliaroni, P. & Ballatore, A. (Eds.) Juxtaposing Thematic Regions Derived from Spatial and Platial User-Generated Content. 13th International Conference on Spatial Information Theory, Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, 2017, 86, 20:1-20:14
Adams, B.; McKenzie, G. & Gahegan, M.: Frankenplace: Interactive Thematic Mapping for Ad Hoc Exploratory Search. Proceedings of the 24th International Conference on World Wide Web, ACM, 2015, 12-22
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.
- Designing a Conversational Travel Recommender System Based on Data-Driven Destination Characterization. ACM RecSys Workshop on Recommenders in Tourism, 2019 mehr…
- How Long to Stay Where? On the Amount of Item Consumption in Travel Recommendation. ACM RecSys 2019 Late-breaking Results, 2019 mehr…
- Tourist Trip Recommendations -- Foundations, State of the Art and Challenges. In: Personalized Human-Computer Interaction. De Gruyter Oldenbourg, 2019, 159–-182 mehr…
- Online Evaluations for Everyone: Mr. DLib's Living Lab for Scholarly Recommendations. Advances in Information Retrieval, Lecture Notes in Computer Science, Springer, 2019 mehr…
- Code Process Metrics in University Programming Education. 2nd Workshop on Innovative Software Engineering Education, 2019 mehr…
- Analyzing the Importance of JabRef Features from the User Perspective. Proceedings of the 11h Central European Workshop on Services and their Composition, 2019 mehr…
- Modeling Physiological Conditions for Proactive Tourist Recommendations. 23rd International Workshop on Personalization and Recommendation on the Web and Beyond, 2019 mehr…
- Identifying Travel Regions Using Location-Based Social Network Check-in Data. Frontiers in Big Data 2, 2019 mehr…
- Deriving Tourist Mobility Patterns from Check-in Data. Proceedings of the WSDM 2018 Workshop on Learning from User Interactions, 2018 mehr…
- Teaching Clean Code. Proceedings of the 1st Workshop on Innovative Software Engineering Education, 2018 mehr…
- Characterisation of Traveller Types Using Check-In Data from Location-Based Social Networks. In: Information and Communication Technologies in Tourism 2019. Springer, 2018 mehr…
- Java by Comparison – Become a Java Craftsman in 70 Examples. The Pragmatic Bookshelf, 2018 mehr…
- Data-Driven Destination Recommender Systems. Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, ACM, 2018 mehr…
- Recommending Crowdsourced Trips on wOndary. Proceedings of the RecSys Workshop on Recommenders in Tourism, 2018 mehr…
- 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 mehr…
- Gesellschaft für Informatik (GI) e.V.
- Association for Computing Machinery (ACM)