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1
. Christoph Trattner 23.10.2015 – Bolzano
Studying Online Food Consumption and Production
Patterns: Recent Trends and Challenges
Christoph Trattner
Know-Center
@Know-Center, TUG, Austria
2
. Christoph Trattner 23.10.2015 – Bolzano
Outline
 Short background info
 Why is the topic important
 Research on Consumption Patterns
 Research on Production Patterns
 Cultural Differences
 Challenges
 Funding opportunities – H2020
3
. Christoph Trattner 23.10.2015 – Bolzano
Where do I come from (Austria)?
4
. Christoph Trattner 23.10.2015 – Bolzano
Graz
5
. Christoph Trattner 23.10.2015 – Bolzano
Academic Background & Working Exp.
 Started in 2004 with my studies at TUG - finished in 2008
 In 2009 I started with my PhD at TUG - finished in Oct.
2012
 After that I worked at the Know-Center (Research Center
for Big Data Analytics ) until Sept. 2014
 From Oct 2014 until Sept. 2015 I worked as Marie Curie
Alain Bensoussan Fellow at NTNU
 Research visits to Yahoo! Labs & CWI
 Since 1st of Oct. 2015 back to Know-Center
6
. Christoph Trattner 23.10.2015 – Bolzano
Why is research on food important?
7
. Christoph Trattner 23.10.2015 – Bolzano
Importance (1)
 Food is one the main concepts that shapes how
good we feel and how healthy we are
 According to the WHO, if common lifestyle risk
factors, among others diet-related ones, were
eliminated, around 80% of cases of heart disease,
strokes and type 2 diabetes, and 40% of cancers,
could be avoided (European Comission
Recommendation C(2010) 2587 final, 2010).
8
. Christoph Trattner 23.10.2015 – Bolzano
Importance (2)
 According to the WHO, within the last three decades
overweight and obesity in the EU population rised
dramatically > 30% (especially for the younger
generation)
 Resulting in a cost of approx. € 81 billion a year to
help people with chronic diseases
9
. Christoph Trattner 23.10.2015 – Bolzano
Studies on Food Consumption Patterns
on the Web
10
. Christoph Trattner 23.10.2015 – Bolzano
West, R., White, R. W., & Horvitz, E. (2013, May). From
cookies to cooks: Insights on dietary patterns via
analysis of web usage logs. In Proceedings of the
22nd international conference on World Wide Web
(pp. 1399-1410). International World Wide Web
Conferences Steering Committee.
11
. Christoph Trattner 23.10.2015 – Bolzano
12
. Christoph Trattner 23.10.2015 – Bolzano
13
. Christoph Trattner 23.10.2015 – Bolzano
14
. Christoph Trattner 23.10.2015 – Bolzano
Abbar, S., Mejova, Y., & Weber, I. (2015). You tweet
what you eat: Studying food consumption through
twitter. ACM CHI 2015.
15
. Christoph Trattner 23.10.2015 – Bolzano
Correlation between food mentions on
Twitter & Obese
p=.772
s=.784
Abbar, S., Mejova, Y., & Weber, I. (2015). You tweet what you eat: Studying food consumption through twitter. ACM CHI 2015.
http://www.caloriecount.com/
 50 million tweets
 Food related keywords
16
. Christoph Trattner 23.10.2015 – Bolzano
Obesity vs Diabetes
17
. Christoph Trattner 23.10.2015 – Bolzano
Influence of Social Connections
18
. Christoph Trattner 23.10.2015 – Bolzano
Studies on Food Production Patterns
19
. Christoph Trattner 23.10.2015 – Bolzano
T. Kusmierczyk, C. Trattner, K. Nørvåg: Temporal
Patterns in Online Food Innovation. WWW
Companion 2015: 1345-1350.
T. Kusmierczyk, C. Trattner, K. Nørvåg: Temporality in
Online Food Recipe Consumption and Production.
WWW Companion 2015: 55-56.
T. Kusmierczyk, C. Trattner, K. Nørvåg: Understanding
and Predicting Recipe Uploads in online food
communities. under review.
20
. Christoph Trattner 23.10.2015 – Bolzano
Dataset
http://kochbar.de
years: 2008-2014
- 200k users
- 400k recipes
- social connections
- Groups
- 270 categories
- Ratings, comments,
uploads
20
21
. Christoph Trattner 23.10.2015 – Bolzano
 constant entropy of
ingredients
 continuous growth of
ingredients combinations
complexity
 consequence:
H(combination | ingredients)
grows
21
T. Kusmierczyk, C. Trattner, K. Nørvåg: Temporal Patterns in Online Food Innovation. WWW Companion 2015: 1345-1350
Community Evolution
22
. Christoph Trattner 23.10.2015 – Bolzano
Innovation (1)
Two phases:
1.strong decline
1.slow but steady
increase
22
2010
T. Kusmierczyk, C. Trattner, K. Nørvåg: Temporal Patterns in Online Food Innovation. WWW Companion 2015: 1345-1350
recipe r similarity to other all
recipes r’
23
. Christoph Trattner 23.10.2015 – Bolzano
Innovation (2)
interesting outliers
23
T. Kusmierczyk, C. Trattner, K. Nørvåg: Temporal Patterns in Online Food Innovation. WWW Companion 2015: 1345-1350
24
. Christoph Trattner 23.10.2015 – Bolzano
Temporal Influence
25
. Christoph Trattner 23.10.2015 – Bolzano
Temporal Patterns
25
T. Kusmierczyk, C. Trattner, K. Nørvåg: Temporality in Online Food Recipe Consumption and Production. WWW Companion 2015: 55-56
26
. Christoph Trattner 23.10.2015 – Bolzano
Lifetime of a Recipe
26
T. Kusmierczyk, C. Trattner, K. Nørvåg: Temporality in Online Food Recipe Consumption and Production. WWW Companion 2015: 55-56
27
. Christoph Trattner 23.10.2015 – Bolzano
Historical Influence
28
. Christoph Trattner 23.10.2015 – Bolzano
29
. Christoph Trattner 23.10.2015 – Bolzano
Social Influence
30
. Christoph Trattner 23.10.2015 – Bolzano
31
. Christoph Trattner 23.10.2015 – Bolzano
Geographic Influence
32
. Christoph Trattner 23.10.2015 – Bolzano
33
. Christoph Trattner 23.10.2015 – Bolzano
Ingredient Prediction Task
34
. Christoph Trattner 23.10.2015 – Bolzano
Cultural Influence
35
. Christoph Trattner 23.10.2015 – Bolzano
 Ahn, Y. Y., Ahnert, S. E., Bagrow, J. P., & Barabási, A.
L. (2011). Flavor network and the principles of food
pairing. Scientific reports, 1.
 Laufer, P., Wagner, C., Flöck, F., & Strohmaier, M.
(2015). Mining cross-cultural relations from
Wikipedia-A study of 31 European food cultures.
ACM WebSci.
36
. Christoph Trattner 23.10.2015 – Bolzano
37
. Christoph Trattner 23.10.2015 – Bolzano
Cultural Influence (Bias)
38
. Christoph Trattner 23.10.2015 – Bolzano
39
. Christoph Trattner 23.10.2015 – Bolzano
Cuisines as perceived by countries in Wikipedia
40
. Christoph Trattner 23.10.2015 – Bolzano
And how is the progress in recommender
research?
41
. Christoph Trattner 23.10.2015 – Bolzano
Teng, C. Y., Lin, Y. R., & Adamic, L. A. (2012, June).
Recipe recommendation using ingredient networks.
In Proceedings of the 4th Annual ACM Web Science
Conference (pp. 298-307). ACM.
42
. Christoph Trattner 23.10.2015 – Bolzano
43
. Christoph Trattner 23.10.2015 – Bolzano
Elsweiler, D., & Harvey, M. (2015, September).
Towards Automatic Meal Plan Recommendations for
Balanced Nutrition. In Proceedings of the 9th ACM
Conference on Recommender Systems (pp. 313-
316). ACM
44
. Christoph Trattner 23.10.2015 – Bolzano
 Small user study (100 users over 3 years)
 Goal: predict rating of users according to eating
guidelines
 Personas: age, gender, hight, goal,...
 Findings: In general possible but also not so easy
task
 Hard Profiles: some users tend to only rate highly calorific and fatty
recipes
 very few breakfasts rates
 recipes with a lower diversity of ingredients
 number of recipes they have rated is low
45
. Christoph Trattner 23.10.2015 – Bolzano
Challenges
46
. Christoph Trattner 23.10.2015 – Bolzano
WebScience Research
Recommender Research
Nutrition (Food) Research
Survey based (small scale & expensive) – 100s of papers dealing with
issues related to food & health related issues
Data driven, large scale offline studies, cheap, fast –
hardly any research yet – no evidence of correlation & causation
Mostly data driven, small to large scale offline „studies“, cheap,
Fast – not much evidence yet for usefulness
47
. Christoph Trattner 23.10.2015 – Bolzano
Is there also funding for this kind of
research?
48
. Christoph Trattner 23.10.2015 – Bolzano
H2020:
Food Scanner Challenge
Web:
http://ec.europa.eu/research/horizonprize/index.c
m?prize=food-scanner
Social Media:
https://twitter.com/EU_eHealth
Video:
https://www.youtube.com/watch?v=v0uggsj4Ars
49
. Christoph Trattner 23.10.2015 – Bolzano
H2020 - WPs
Health, demographic change and well-being
 SC1-PM-15-2017: Personalised coaching for well-being and care of
people as they age
 SC1-PM-17–2017: Personalised computer models and in-silico
systems for well-being
 SC1-PM-05–2016: The European Human Biomonitoring Initiative
Information and Communication Technologies
 ICT-11-2017: Collective Awareness Platforms for Sustainability and
Social Innovation
 ICT-19-2017: Media and content convergence
Food security, sustainable agriculture and forestry,
marine and maritime and inland water research and
the bioeconomy
50
. Christoph Trattner 23.10.2015 – Bolzano
The Sustainable Food Security call will address resilience
and resource efficiency in the primary sectors (agriculture, forestry,
fisheries and aquaculture) and in the related up- and downstream
industries to ensure the food and nutritional security of EU citizens.
Investments in innovation will support stability and competitiveness of
the agri-food chains, such as the food industry, the largest EU
manufacturing industry. This call will also help to safeguard and make
efficient use of the natural capital as the basis of primary sectors, while
factoring in climate and environmental challenges. Finally, the
call will explore innovative approaches in the
food value chain to empower citizens to change
towards sustainable and healthy food
consumption patterns and lifestyles.
51
. Christoph Trattner 23.10.2015 – Bolzano
H2020 - WPs
 FET Open supports the early-stages of the science
and technology research
 FET Proactive addresses promising directions for
research to build up a European critical mass of
knowledge and excellence around them.
 FET Flagships are science-driven, large-scale,
multidisciplinary research initiatives
Opening: 08 Dec. 2015
Budget: 84.00 (2016)
Deadline: 11 May 2016
http://ec.europa.eu/research/participants/data/ref/h2020/wp/2016_2017/main/h2020-wp1617-fet_en.pdf
52
. Christoph Trattner 23.10.2015 – Bolzano
People/Institutions interested
 L3S Research Center, Germany
 PUC, Chile
 NTNU, Norway
 CWI, The Netherlands
 University of Tallinn, Estonia
 GESIS, Germany
 Yahoo Labs!, UK
 University of Bolzano, Italy
 Graz University of Technology, Austria
 MedUni Graz, Austria
 University of Regensburg, Germany
 Qatar University, Qatar
53
. Christoph Trattner 23.10.2015 – Bolzano
Thank you!
Christoph Trattner
Email: trattner.christoph@gmail.com
Web: christophtrattner.info
Twitter: @ctrattner
54
. Christoph Trattner 23.10.2015 – Bolzano
References
 Kusmierczyk, T., Trattner, C., & Nørvåg, K. (2015, May). Temporality in online food recipe consumption and production. In
Proceedings of the 24th International Conference on World Wide Web Companion (pp. 55-56). International World Wide Web
Conferences Steering Committee.
 Kusmierczyk, T., Trattner, C., & Nørvåg, K. (2015, May). Temporal Patterns in Online Food Innovation. In Proceedings of the 24th
International Conference on World Wide Web Companion (pp. 1345-1350). International World Wide Web Conferences Steering
Committee.
 Wagner, C., Singer, P., & Strohmaier, M. (2014). The nature and evolution of online food preferences. EPJ Data Science, 3(1), 1-
22.
 Laufer, P., Wagner, C., Flöck, F., & Strohmaier, M. (2015). Mining cross-cultural relations from Wikipedia-A study of 31 European
food cultures. ACM WebSci.
 Rokicki, M., Herder, E., & Demidova, E. (2015). What’s On My Plate: Towards Recommending Recipe Variations for Diabetes
Patients. Extended proc. user modeling, adaptation and personalizationumap 2015.
 Elsweiler, D., & Harvey, M. (2015, September). Towards Automatic Meal Plan Recommendations for Balanced Nutrition. In
Proceedings of the 9th ACM Conference on Recommender Systems (pp. 313-316). ACM.
 Said, A., & Bellogín, A. (2014). You are what you eat! tracking health through recipe interactions. Proc. of RSWeb, 14.
 Abbar, S., Mejova, Y., & Weber, I. (2014). You tweet what you eat: Studying food consumption through twitter. arXiv preprint
arXiv:1412.4361.
 Mejova, Y., Haddadi, H., Noulas, A., & Weber, I. (2015, May). # FoodPorn: Obesity Patterns in Culinary Interactions. In
Proceedings of the 5th International Conference on Digital Health 2015 (pp. 51-58). ACM.
 Teng, C. Y., Lin, Y. R., & Adamic, L. A. (2012, June). Recipe recommendation using ingredient networks. In Proceedings of the
4th Annual ACM Web Science Conference (pp. 298-307). ACM.
 Ge, M., Ricci, F., & Massimo, D. (2015, September). Health-aware Food Recommender System. In Proceedings of the 9th ACM
Conference on Recommender Systems (pp. 333-334). ACM.
 Ahn, Y. Y., Ahnert, S. E., Bagrow, J. P., & Barabási, A. L. (2011). Flavor network and the principles of food pairing. Scientific
reports, 1.
 Freyne, J., & Berkovsky, S. (2010, February). Intelligent food planning: personalized recipe recommendation. In Proceedings of
the 15th international conference on Intelligent user interfaces (pp. 321-324). ACM.
 Elahi, M., Ge, M., Ricci, F., Berkovsky, S., & David, M. (2015) Interaction Design in a Mobile Food Recommender System.

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Studying Online Food Consumption and Production Patterns: Recent Trends and Challenges

  • 1. 1 . Christoph Trattner 23.10.2015 – Bolzano Studying Online Food Consumption and Production Patterns: Recent Trends and Challenges Christoph Trattner Know-Center @Know-Center, TUG, Austria
  • 2. 2 . Christoph Trattner 23.10.2015 – Bolzano Outline  Short background info  Why is the topic important  Research on Consumption Patterns  Research on Production Patterns  Cultural Differences  Challenges  Funding opportunities – H2020
  • 3. 3 . Christoph Trattner 23.10.2015 – Bolzano Where do I come from (Austria)?
  • 4. 4 . Christoph Trattner 23.10.2015 – Bolzano Graz
  • 5. 5 . Christoph Trattner 23.10.2015 – Bolzano Academic Background & Working Exp.  Started in 2004 with my studies at TUG - finished in 2008  In 2009 I started with my PhD at TUG - finished in Oct. 2012  After that I worked at the Know-Center (Research Center for Big Data Analytics ) until Sept. 2014  From Oct 2014 until Sept. 2015 I worked as Marie Curie Alain Bensoussan Fellow at NTNU  Research visits to Yahoo! Labs & CWI  Since 1st of Oct. 2015 back to Know-Center
  • 6. 6 . Christoph Trattner 23.10.2015 – Bolzano Why is research on food important?
  • 7. 7 . Christoph Trattner 23.10.2015 – Bolzano Importance (1)  Food is one the main concepts that shapes how good we feel and how healthy we are  According to the WHO, if common lifestyle risk factors, among others diet-related ones, were eliminated, around 80% of cases of heart disease, strokes and type 2 diabetes, and 40% of cancers, could be avoided (European Comission Recommendation C(2010) 2587 final, 2010).
  • 8. 8 . Christoph Trattner 23.10.2015 – Bolzano Importance (2)  According to the WHO, within the last three decades overweight and obesity in the EU population rised dramatically > 30% (especially for the younger generation)  Resulting in a cost of approx. € 81 billion a year to help people with chronic diseases
  • 9. 9 . Christoph Trattner 23.10.2015 – Bolzano Studies on Food Consumption Patterns on the Web
  • 10. 10 . Christoph Trattner 23.10.2015 – Bolzano West, R., White, R. W., & Horvitz, E. (2013, May). From cookies to cooks: Insights on dietary patterns via analysis of web usage logs. In Proceedings of the 22nd international conference on World Wide Web (pp. 1399-1410). International World Wide Web Conferences Steering Committee.
  • 11. 11 . Christoph Trattner 23.10.2015 – Bolzano
  • 12. 12 . Christoph Trattner 23.10.2015 – Bolzano
  • 13. 13 . Christoph Trattner 23.10.2015 – Bolzano
  • 14. 14 . Christoph Trattner 23.10.2015 – Bolzano Abbar, S., Mejova, Y., & Weber, I. (2015). You tweet what you eat: Studying food consumption through twitter. ACM CHI 2015.
  • 15. 15 . Christoph Trattner 23.10.2015 – Bolzano Correlation between food mentions on Twitter & Obese p=.772 s=.784 Abbar, S., Mejova, Y., & Weber, I. (2015). You tweet what you eat: Studying food consumption through twitter. ACM CHI 2015. http://www.caloriecount.com/  50 million tweets  Food related keywords
  • 16. 16 . Christoph Trattner 23.10.2015 – Bolzano Obesity vs Diabetes
  • 17. 17 . Christoph Trattner 23.10.2015 – Bolzano Influence of Social Connections
  • 18. 18 . Christoph Trattner 23.10.2015 – Bolzano Studies on Food Production Patterns
  • 19. 19 . Christoph Trattner 23.10.2015 – Bolzano T. Kusmierczyk, C. Trattner, K. Nørvåg: Temporal Patterns in Online Food Innovation. WWW Companion 2015: 1345-1350. T. Kusmierczyk, C. Trattner, K. Nørvåg: Temporality in Online Food Recipe Consumption and Production. WWW Companion 2015: 55-56. T. Kusmierczyk, C. Trattner, K. Nørvåg: Understanding and Predicting Recipe Uploads in online food communities. under review.
  • 20. 20 . Christoph Trattner 23.10.2015 – Bolzano Dataset http://kochbar.de years: 2008-2014 - 200k users - 400k recipes - social connections - Groups - 270 categories - Ratings, comments, uploads 20
  • 21. 21 . Christoph Trattner 23.10.2015 – Bolzano  constant entropy of ingredients  continuous growth of ingredients combinations complexity  consequence: H(combination | ingredients) grows 21 T. Kusmierczyk, C. Trattner, K. Nørvåg: Temporal Patterns in Online Food Innovation. WWW Companion 2015: 1345-1350 Community Evolution
  • 22. 22 . Christoph Trattner 23.10.2015 – Bolzano Innovation (1) Two phases: 1.strong decline 1.slow but steady increase 22 2010 T. Kusmierczyk, C. Trattner, K. Nørvåg: Temporal Patterns in Online Food Innovation. WWW Companion 2015: 1345-1350 recipe r similarity to other all recipes r’
  • 23. 23 . Christoph Trattner 23.10.2015 – Bolzano Innovation (2) interesting outliers 23 T. Kusmierczyk, C. Trattner, K. Nørvåg: Temporal Patterns in Online Food Innovation. WWW Companion 2015: 1345-1350
  • 24. 24 . Christoph Trattner 23.10.2015 – Bolzano Temporal Influence
  • 25. 25 . Christoph Trattner 23.10.2015 – Bolzano Temporal Patterns 25 T. Kusmierczyk, C. Trattner, K. Nørvåg: Temporality in Online Food Recipe Consumption and Production. WWW Companion 2015: 55-56
  • 26. 26 . Christoph Trattner 23.10.2015 – Bolzano Lifetime of a Recipe 26 T. Kusmierczyk, C. Trattner, K. Nørvåg: Temporality in Online Food Recipe Consumption and Production. WWW Companion 2015: 55-56
  • 27. 27 . Christoph Trattner 23.10.2015 – Bolzano Historical Influence
  • 28. 28 . Christoph Trattner 23.10.2015 – Bolzano
  • 29. 29 . Christoph Trattner 23.10.2015 – Bolzano Social Influence
  • 30. 30 . Christoph Trattner 23.10.2015 – Bolzano
  • 31. 31 . Christoph Trattner 23.10.2015 – Bolzano Geographic Influence
  • 32. 32 . Christoph Trattner 23.10.2015 – Bolzano
  • 33. 33 . Christoph Trattner 23.10.2015 – Bolzano Ingredient Prediction Task
  • 34. 34 . Christoph Trattner 23.10.2015 – Bolzano Cultural Influence
  • 35. 35 . Christoph Trattner 23.10.2015 – Bolzano  Ahn, Y. Y., Ahnert, S. E., Bagrow, J. P., & Barabási, A. L. (2011). Flavor network and the principles of food pairing. Scientific reports, 1.  Laufer, P., Wagner, C., Flöck, F., & Strohmaier, M. (2015). Mining cross-cultural relations from Wikipedia-A study of 31 European food cultures. ACM WebSci.
  • 36. 36 . Christoph Trattner 23.10.2015 – Bolzano
  • 37. 37 . Christoph Trattner 23.10.2015 – Bolzano Cultural Influence (Bias)
  • 38. 38 . Christoph Trattner 23.10.2015 – Bolzano
  • 39. 39 . Christoph Trattner 23.10.2015 – Bolzano Cuisines as perceived by countries in Wikipedia
  • 40. 40 . Christoph Trattner 23.10.2015 – Bolzano And how is the progress in recommender research?
  • 41. 41 . Christoph Trattner 23.10.2015 – Bolzano Teng, C. Y., Lin, Y. R., & Adamic, L. A. (2012, June). Recipe recommendation using ingredient networks. In Proceedings of the 4th Annual ACM Web Science Conference (pp. 298-307). ACM.
  • 42. 42 . Christoph Trattner 23.10.2015 – Bolzano
  • 43. 43 . Christoph Trattner 23.10.2015 – Bolzano Elsweiler, D., & Harvey, M. (2015, September). Towards Automatic Meal Plan Recommendations for Balanced Nutrition. In Proceedings of the 9th ACM Conference on Recommender Systems (pp. 313- 316). ACM
  • 44. 44 . Christoph Trattner 23.10.2015 – Bolzano  Small user study (100 users over 3 years)  Goal: predict rating of users according to eating guidelines  Personas: age, gender, hight, goal,...  Findings: In general possible but also not so easy task  Hard Profiles: some users tend to only rate highly calorific and fatty recipes  very few breakfasts rates  recipes with a lower diversity of ingredients  number of recipes they have rated is low
  • 45. 45 . Christoph Trattner 23.10.2015 – Bolzano Challenges
  • 46. 46 . Christoph Trattner 23.10.2015 – Bolzano WebScience Research Recommender Research Nutrition (Food) Research Survey based (small scale & expensive) – 100s of papers dealing with issues related to food & health related issues Data driven, large scale offline studies, cheap, fast – hardly any research yet – no evidence of correlation & causation Mostly data driven, small to large scale offline „studies“, cheap, Fast – not much evidence yet for usefulness
  • 47. 47 . Christoph Trattner 23.10.2015 – Bolzano Is there also funding for this kind of research?
  • 48. 48 . Christoph Trattner 23.10.2015 – Bolzano H2020: Food Scanner Challenge Web: http://ec.europa.eu/research/horizonprize/index.c m?prize=food-scanner Social Media: https://twitter.com/EU_eHealth Video: https://www.youtube.com/watch?v=v0uggsj4Ars
  • 49. 49 . Christoph Trattner 23.10.2015 – Bolzano H2020 - WPs Health, demographic change and well-being  SC1-PM-15-2017: Personalised coaching for well-being and care of people as they age  SC1-PM-17–2017: Personalised computer models and in-silico systems for well-being  SC1-PM-05–2016: The European Human Biomonitoring Initiative Information and Communication Technologies  ICT-11-2017: Collective Awareness Platforms for Sustainability and Social Innovation  ICT-19-2017: Media and content convergence Food security, sustainable agriculture and forestry, marine and maritime and inland water research and the bioeconomy
  • 50. 50 . Christoph Trattner 23.10.2015 – Bolzano The Sustainable Food Security call will address resilience and resource efficiency in the primary sectors (agriculture, forestry, fisheries and aquaculture) and in the related up- and downstream industries to ensure the food and nutritional security of EU citizens. Investments in innovation will support stability and competitiveness of the agri-food chains, such as the food industry, the largest EU manufacturing industry. This call will also help to safeguard and make efficient use of the natural capital as the basis of primary sectors, while factoring in climate and environmental challenges. Finally, the call will explore innovative approaches in the food value chain to empower citizens to change towards sustainable and healthy food consumption patterns and lifestyles.
  • 51. 51 . Christoph Trattner 23.10.2015 – Bolzano H2020 - WPs  FET Open supports the early-stages of the science and technology research  FET Proactive addresses promising directions for research to build up a European critical mass of knowledge and excellence around them.  FET Flagships are science-driven, large-scale, multidisciplinary research initiatives Opening: 08 Dec. 2015 Budget: 84.00 (2016) Deadline: 11 May 2016 http://ec.europa.eu/research/participants/data/ref/h2020/wp/2016_2017/main/h2020-wp1617-fet_en.pdf
  • 52. 52 . Christoph Trattner 23.10.2015 – Bolzano People/Institutions interested  L3S Research Center, Germany  PUC, Chile  NTNU, Norway  CWI, The Netherlands  University of Tallinn, Estonia  GESIS, Germany  Yahoo Labs!, UK  University of Bolzano, Italy  Graz University of Technology, Austria  MedUni Graz, Austria  University of Regensburg, Germany  Qatar University, Qatar
  • 53. 53 . Christoph Trattner 23.10.2015 – Bolzano Thank you! Christoph Trattner Email: trattner.christoph@gmail.com Web: christophtrattner.info Twitter: @ctrattner
  • 54. 54 . Christoph Trattner 23.10.2015 – Bolzano References  Kusmierczyk, T., Trattner, C., & Nørvåg, K. (2015, May). Temporality in online food recipe consumption and production. In Proceedings of the 24th International Conference on World Wide Web Companion (pp. 55-56). International World Wide Web Conferences Steering Committee.  Kusmierczyk, T., Trattner, C., & Nørvåg, K. (2015, May). Temporal Patterns in Online Food Innovation. In Proceedings of the 24th International Conference on World Wide Web Companion (pp. 1345-1350). International World Wide Web Conferences Steering Committee.  Wagner, C., Singer, P., & Strohmaier, M. (2014). The nature and evolution of online food preferences. EPJ Data Science, 3(1), 1- 22.  Laufer, P., Wagner, C., Flöck, F., & Strohmaier, M. (2015). Mining cross-cultural relations from Wikipedia-A study of 31 European food cultures. ACM WebSci.  Rokicki, M., Herder, E., & Demidova, E. (2015). What’s On My Plate: Towards Recommending Recipe Variations for Diabetes Patients. Extended proc. user modeling, adaptation and personalizationumap 2015.  Elsweiler, D., & Harvey, M. (2015, September). Towards Automatic Meal Plan Recommendations for Balanced Nutrition. In Proceedings of the 9th ACM Conference on Recommender Systems (pp. 313-316). ACM.  Said, A., & Bellogín, A. (2014). You are what you eat! tracking health through recipe interactions. Proc. of RSWeb, 14.  Abbar, S., Mejova, Y., & Weber, I. (2014). You tweet what you eat: Studying food consumption through twitter. arXiv preprint arXiv:1412.4361.  Mejova, Y., Haddadi, H., Noulas, A., & Weber, I. (2015, May). # FoodPorn: Obesity Patterns in Culinary Interactions. In Proceedings of the 5th International Conference on Digital Health 2015 (pp. 51-58). ACM.  Teng, C. Y., Lin, Y. R., & Adamic, L. A. (2012, June). Recipe recommendation using ingredient networks. In Proceedings of the 4th Annual ACM Web Science Conference (pp. 298-307). ACM.  Ge, M., Ricci, F., & Massimo, D. (2015, September). Health-aware Food Recommender System. In Proceedings of the 9th ACM Conference on Recommender Systems (pp. 333-334). ACM.  Ahn, Y. Y., Ahnert, S. E., Bagrow, J. P., & Barabási, A. L. (2011). Flavor network and the principles of food pairing. Scientific reports, 1.  Freyne, J., & Berkovsky, S. (2010, February). Intelligent food planning: personalized recipe recommendation. In Proceedings of the 15th international conference on Intelligent user interfaces (pp. 321-324). ACM.  Elahi, M., Ge, M., Ricci, F., Berkovsky, S., & David, M. (2015) Interaction Design in a Mobile Food Recommender System.