studying online food consumption and production patterns: recent trends and challenges

1

. Christoph Trattner 23.10.2015 – Bolzano

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

Christoph TrattnerKnow-Center

@Know-Center, TUG, Austria

2


Outline Short background info

Why is the topic important

Research on Consumption Patterns

Research on Production Patterns

Cultural Differences

Challenges

Funding opportunities – H2020

3


Where do I come from (Austria)?

4


Graz

5


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


Why is research on food important?

7


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


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


Studies on Food Consumption Patterns on the Web

10


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


12


13


14


Abbar, S., Mejova, Y., & Weber, I. (2015). You tweet what you eat: Studying food consumption through twitter. ACM CHI 2015.

15


Correlation between food mentions on Twitter & Obese

p=.772s=.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


Obesity vs Diabetes

17


Influence of Social Connections

18


Studies on Food Production Patterns

19


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


Dataset

http://kochbar.de

years: 2008-2014- 200k users- 400k recipes- social connections- Groups- 270 categories- Ratings, comments,

uploads

20

http://kochbar.de/

21


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


Innovation (1)

Two phases:

1.strong decline

2.slow but steady increase

22

2010


recipe r similarity to other all recipes r’

23


Innovation (2)

interesting outliers

23


24


Temporal Influence

25


Temporal Patterns

25

T. Kusmierczyk, C. Trattner, K. Nørvåg: Temporality in Online Food Recipe Consumption and Production. WWW Companion 2015: 55-56

26


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


Historical Influence

28


29


Social Influence

30


31


Geographic Influence

32


33


Ingredient Prediction Task

34


Cultural Influence

35


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


37


Cultural Influence (Bias)

38


39


Cuisines as perceived by countries in Wikipedia

40


And how is the progress in recommender research?

41


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


43


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


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


Challenges

46


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


Is there also funding for this kind of research?

48


H2020:Food Scanner Challenge

Web:http://ec.europa.eu/research/horizonprize/index.cfm?prize=food-scanner

Social Media:https://twitter.com/EU_eHealth

Video:https://www.youtube.com/watch?v=v0uggsj4Ars

http://ec.europa.eu/research/horizonprize/index.cfm?prize=food-scanner



https://twitter.com/EU_eHealth

https://twitter.com/EU_eHealth

https://www.youtube.com/watch?v=v0uggsj4Ars



49


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


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


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. 2015Budget: 84.00 (2016)Deadline: 11 May 2016http://ec.europa.eu/research/participants/data/ref/h2020/wp/2016_2017/main/h2020-wp1617-fet_en.pdf

52


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


Thank you!

Christoph Trattner

Email: [email protected]: christophtrattner.info

Twitter: @ctrattner

54


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.

studying online food consumption and production patterns: recent trends and challenges

Data & Analytics