Dr. Christoph Trattner is currently working as an Asst. Prof. at MODUL University Vienna in the New Media Technology Department. Previously to that (2013-2016) he was an area manager at the Know-Center, Austria's research competence for data driven business and BigData analytics where he founded and lead the Social Computing area.
He holds a PhD (cum laude), a MSc (cum laude) and a BSc in Computer Science and Telematics from Graz University of Technology (Austria). He is a former FFG, Marshall Plan and ERCIM fellow and has been working at Graz University of Technology from 2009-2012, the University of Pittsburgh from 2011-2012, the Norwegian University of Science and Technology from 2014-2015, and has been visiting Yahoo! Labs Barcelona in 2014 and CWI Amsterdam in 2015 (two times).
For more information, visit his website: http://christophtrattner.info
Christoph's research interests include AI, Machine Learning, Recommender Systems and the Science of the Web. He was involved, either as a collaborator or a project leader, in various national and international EU-funded research projects that dealt with social technologies and recommender systems. Currently, he is driving a research project that tries to understand, predict and change online food preferences to tackle health-related food issues such as diabetis or obese.
Since 2010, he published two books and over 70 scientific articles in top venues and journals, e.g., Wiley JASIST, ACM TiiS, Elsevier ComCom, WWW, ACM WebSci, ACM CIKM, ACM CSCW, ACM RecSys, ACM IUI, ACM HT and ACM UMAP. He is also the winner of several Best Paper/Poster Awards and Nominations, including e.g. the Best Paper Award Honorable Mention at WWW'17. He regularly acts as a PC member on several top-tier conferences and co-organizes or co-chaires a number of workshops and conferences. Recent examples include WWW'17 or ACM RecSys'17.
Selected Publications (Last 5 years)
- Exploiting Food Choice Biases for Healthier Recipe Recommendation. Elsweiler, D.*, Trattner, C.* and Harvey, M. (* equal contribution). In Proceedings of the ACM SIGIR Conference (SIGIR), 2017. PDF
- Investigating the Healthiness of Internet-Sourced Recipes: Implications for Meal Planning and Recommender Systems. Trattner, C. and Elsweiler, D. In Proceedings of the World Wide Web Conference (WWW), 2017. Best Paper Honorable Mention Award PDF
- Modeling Activation Processes in Human Memory to Predict the Use of Tags in Social Bookmarking Systems. Trattner, C., Kowald, D., Seitlinger, P., Kopeinik, S. and Ley, T. The Journal of Web Science (JWS), Volume 2, Issue 1, 2016. PDF
- VizRec: Recommending Personalized Visualizations. Mutlu, B., Veas, E. and Trattner, C. ACM Transactions on Interactive Intelligent Systems (TiiS), Volume 6, Issue 4, 2016. PDF
- Twitter in Academic Events: A Study of Temporal Usage, Communication, Sentimental and Topical Patterns in 16 Computer Science Conferences. Parra, D., Trattner, C., Gomez, M., Hurtado, D., Wen, X. and Lin, Y. Computer Communications Journal (COMCOM), Volume 73, Part B, 2016. PDF
- Detecting Partnership in Online and Location-based Social Networks, Trattner, C. and Steurer, M. 2015. Social Network Analysis and Mining (SNAM), Volume 5, Issue 32, 2015. PDF
- The Impact of Image Descriptions on User Tagging Behavior: A Study of the Nature and Functionality of Crowdsourced Tags. Lin, Y., Trattner, C., Brusilovsky, P. and He, D. Journal of the Association for Information Science and Technology (JASIST), Volume 66, Issue 9, 2015. PDF
2017 : Annual Best of Computing (ACM Computing Reviews)
2017 : Best Paper Award Honorable Mention at WWW'17 (World Wide Web Consortium)
- Title A-Z
- Title Z-A
- Newest Publication
- Oldest Publication
- Newest Modification
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Mohsen Shahriari, Martin Barth, Ralf Klamma, Christoph Trattner"TCNSVD: A Temporal and Community-Aware Recommender Approach"2017
Author(s): Mohsen Shahriari, Martin Barth, Ralf Klamma, Christoph Trattner
Publication date: 8. 2017
Christoph Trattner, Denis Parra, David Elsweiler"Monitoring obesity prevalence in the United States through bookmarking activities in online food portals"2017 in: Plos ONE. Volume: 12. Issue number: 6
Studying the impact of food consumption on people’s health is a serious matter for its implications on public policy, but it has traditionally been a slow process since it requires information gathered through expensive collection processes such as surveys, census and systematic reviews of research articles. We argue that this process could be supported and hastened using data collected via online social networks. In this work we investigate the relationships between the online traces left behind by users of a large US online food community and the prevalence of obesity in 47 states and 311 counties in the US. Using data associated with the recipes bookmarked over an 9-year period by 144,839 users of the Allrecipes.com food website residing throughout the US, several hierarchical regression models are created to (i) shed light on these relations and (ii) establish their magnitude. The results of our analysis provide strong evidence that bookmarking activities on recipes in online food communities can provide a signal allowing food and health related issues, such as obesity to be better understood and monitored. We discover that higher fat and sugar content in bookmarked recipes is associated with higher rates of obesity. The dataset is complicated, but strong temporal and geographical trends are identifiable. We show the importance of accounting for these trends in the modeling process.
Author(s): Christoph Trattner, Denis Parra, David Elsweiler
Publication date: 21. 6. 2017
Issue number: 6
Electronic version(s), related files and links: http://dx.doi.org/10.1371/journal.pone.0179144
Belgin Mutlu, Eduardo Veas, Christoph Trattner"Tags, Titles or Q&As? Choosing Content Descriptors for Visual Recommender Systems"2017 Pages: 265-274
In today's digital age with an increasing number of websites, social/learning platforms, and different computer-mediated communication systems, finding valuable information is a challenging and tedious task, regardless from which discipline a person is. However, visualizations have shown to be effective in dealing with huge datasets: because they are grounded on visual cognition, people understand them and can naturally perform visual operations such as clustering, filtering and comparing quantities. But, creating appropriate visual representations of data is also challenging: it requires domain knowledge, understanding of the data, and knowledge about task and user preferences. To tackle this issue, we have developed a recommender system that generates visualizations based on (i) a set of visual cognition rules/guidelines, and (ii) filters a subset considering user preferences. A user places interests on several aspects of a visualization, the task or problem it helps to solve, the operations it permits, or the features of the dataset it represents. This paper concentrates on characterizing user preferences, in particular: i) the sources of information used to describe the visualizations, the content descriptors respectively, and ii) the methods to produce the most suitable recommendations thereby. We consider three sources corresponding to different aspects of interest: a title that describes the chart, a question that can be answered with the chart (and the answer), and a collection of tags describing features of the chart. We investigate user-provided input based on these sources collected with a crowd-sourced study. Firstly, information-theoretic measures are applied to each source to determine the efficiency of the input in describing user preferences and visualization contents (user and item models). Secondly, the practicability of each input is evaluated with content-based recommender system. The overall methodology and results contribute methods for design and analysis of visual recommender systems. The findings in this paper highlight the inputs which can (i) effectively encode the content of the visualizations and user's visual preferences/interest, and (ii) are more valuable for recommending personalized visualizations.
Author(s): Belgin Mutlu, Eduardo Veas, Christoph Trattner
Publication date: 6. 2017
Christoph Trattner, David Elsweiler, Howard Simon"Estimating the Healthiness of Internet Recipes: A Cross-Sectional Study"2017 in: Frontiers in Public Health. Volume: 5. Issue number: 16
A government’s response to increasing incidence of lifestyle-related illnesses, such as obesity, has been to encourage people to cook for themselves. The healthiness of home cooking will, nevertheless, depend on what people cook and how they cook it. In this article, one common source of cooking inspiration—Internet-sourced recipes—is investigated in depth. The energy and macronutrient content of 5,237 main meal recipes from the food website Allrecipes.com are compared with those of 100 main meal recipes from five bestselling cookery books from popular celebrity chefs and 100 ready meals from the three leading UK supermarkets. The comparison is made using nutritional guidelines published by the World Health Organization and the UK Food Standards Agency. The main conclusions drawn from our analyses are that Internet recipes sourced from Allrecipes.com are less healthy than TV chef recipes and ready meals from leading UK supermarkets. Only 6 out of 5,237 Internet recipes fully complied with the WHO recommendations. Internet recipes were more likely to meet the WHO guidelines for protein than other classes of meal (10.88 v 7% (TV), p < 0.01; 10.86 v 9% (ready), p < 0.01). However, the Internet recipes were less likely to meet the criteria for fat (14.28 v 24 (TV) v 37% (ready); p < 0.01), saturated fat (25.05 v 33 (TV) v 34% (ready); p < 0.01), and fiber (compared to ready meals 16.50 v 56%; p < 0.01). More Internet recipes met the criteria for sodium density than ready meals (19.63 v 4%; p < 0.01), but fewer than the TV chef meals (19.32 v 36%; p < 0.01). For sugar, no differences between Internet recipes and TV chef recipes were observed (81.1 v 81% (TV); p = 0.86), although Internet recipes were less likely to meet the sugar criteria than ready meals (81.1 v 83% (ready); p < 0.01). Repeating the analyses for each year of available data shows that the results are very stable over time.
Author(s): Christoph Trattner, David Elsweiler, Howard Simon
Publication date: 13. 2. 2017
Issue number: 16
Electronic version(s), related files and links: http://dx.doi.org/10.3389/fpubh.2017.00016
Christoph Trattner, David Elsweiler"Investigating the Healthiness of Internet-Sourced Recipes: Implications for Meal Planning and Recommender Systems"2017
Author(s): Christoph Trattner, David Elsweiler
Publication date: 2017
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