Food is a fundamental concept in our daily lives and is one of the most important factors that shape how healthy we are or how good we feel. Although research on the users’ food preferences has been a well-established research area over the last decades, only very little research was devoted yet to understand, how the World Wide Web influences the way we consume or produce food offline.
In this talk, I will therefore highlight recent research in online food communities and interesting findings in terms of online food recipe consumption and production patterns. I will show, how these studies might be useful when drawing conclusions about health related issues, such as obesity or diabetes of a large population, and how these insights might be used to tune current recommender approaches in this domain.
Last but not least, I will discuss the limitations of these studies and will highlight the need for a joined European taskforce, that studies food, health related issues and recommender systems on a much larger and more useful way, as proposed by current research in this area.
Recommending Items in Social Tagging Systems Using Tag and Time InformationChristoph Trattner
In this work we present a novel item recommendation ap- proach that aims at improving Collaborative Filtering (CF) in social tagging systems using the information about tags and time. Our algorithm follows a two-step approach, where in the first step a potentially interesting candidate item-set is found using user-based CF and in the second step this can- didate item-set is ranked using item-based CF. Within this ranking step we integrate the information of tag usage and time using the Base-Level Learning (BLL) equation com- ing from human memory theory that is used to determine the reuse-probability of words and tags using a power-law forgetting function.
As the results of our extensive evaluation conducted on data- sets gathered from three social tagging systems (BibSonomy, CiteULike and MovieLens) show, the usage of tag-based and time information via the BLL equation also helps to improve the ranking and recommendation process of items and thus, can be used to realize an effective item recommender that outperforms two alternative algorithms which also exploit time and tag-based information.
From Search to Predictions in Tagged Information SpacesChristoph Trattner
Tagging gained tremendously in popularity over past few years. When looking into the literature of tagging we find a lot of work regarding people's tagging motivation, their behavior, models that describe the folksonomy generation process, emergent semantic structures, etc., but interestingly we find quite little research showing the value of tags for searching an overloaded information space. Furthermore, there is lot of literature on the tag or item prediction problem, but interestingly almost all of them lookat the issue from a data-driven perspective. To bridge this gap in the literature, we have conducted several in-depth studies in the past showing the value of tags for lookup and exploratory search. We looked at the problem from a network theoretic and interface perspective and we will show how useful tags are for searching. Furthermore, we reviewed literature on memory processes from cognitive science and have invented a number of novel recommender algorithms based on the ACT-R and MINERVA2 theory. We will show that these approaches can not only predict tags and items extremely well, but also reveal how these models can help in explaining the recommendation processes better than current approaches.
Je t’aime… moi non plus: reporting on the opportunities, expectations and cha...Christoph Trattner
This presentation will be a live exchange of ideas & arguments, between a representative of a start up working on agricultural information management and discovery, and a representative of academia that has recently completed his PhD and is now leading a young and promising research team.
The two presenters will focus on the case of a recommendation service that is going to be part of a web portal for organic agriculture researchers and educators (called Organic.Edunet), which will help users find relevant educational material and bibliography. They currently develop this as part of an EU-funded initiative but would both be interested to find a way to further sustain this work: the start up by including this to the bundle of services that it offers to the users of its information discovery packages, and the research team by attracting more funding to further explore recommendation technologies.
The start up representative will describe his evergoing, helpless and aimless efforts to include a research activity on recommender systems within the R&D strategy of the company, for the sakes of the good-old-PhD-times. And will explain why this failed.
The academia representative will describe the great things that his research can do to boost the performance of recommendation services in such portals. And why this does-not-work-yet-operationally because he cannot find real usage data that can prove his amazing algorithm outside what can be proven in offline lab experiments using datasets from other domains (like MovieLens and CiteULike).
Both will explain how they started working together in order to design, experimentally test, and deploy the Organic.Edunet recommendation service. And will describe their expectations from this academic-industry collaboration. Then, they will reflect on the challenges they see in such partnerships and how (if) they plan to overcome them.
Keystone summer school 2015 paolo-missier-provenancePaolo Missier
Lecture on Provenance modelling, given at the first Keystone Summer School, Malta July 2015.
With thanks to Prof. Luc Moreau for contributing some of the slide material from his own tutorial
Recommending Items in Social Tagging Systems Using Tag and Time InformationChristoph Trattner
In this work we present a novel item recommendation ap- proach that aims at improving Collaborative Filtering (CF) in social tagging systems using the information about tags and time. Our algorithm follows a two-step approach, where in the first step a potentially interesting candidate item-set is found using user-based CF and in the second step this can- didate item-set is ranked using item-based CF. Within this ranking step we integrate the information of tag usage and time using the Base-Level Learning (BLL) equation com- ing from human memory theory that is used to determine the reuse-probability of words and tags using a power-law forgetting function.
As the results of our extensive evaluation conducted on data- sets gathered from three social tagging systems (BibSonomy, CiteULike and MovieLens) show, the usage of tag-based and time information via the BLL equation also helps to improve the ranking and recommendation process of items and thus, can be used to realize an effective item recommender that outperforms two alternative algorithms which also exploit time and tag-based information.
From Search to Predictions in Tagged Information SpacesChristoph Trattner
Tagging gained tremendously in popularity over past few years. When looking into the literature of tagging we find a lot of work regarding people's tagging motivation, their behavior, models that describe the folksonomy generation process, emergent semantic structures, etc., but interestingly we find quite little research showing the value of tags for searching an overloaded information space. Furthermore, there is lot of literature on the tag or item prediction problem, but interestingly almost all of them lookat the issue from a data-driven perspective. To bridge this gap in the literature, we have conducted several in-depth studies in the past showing the value of tags for lookup and exploratory search. We looked at the problem from a network theoretic and interface perspective and we will show how useful tags are for searching. Furthermore, we reviewed literature on memory processes from cognitive science and have invented a number of novel recommender algorithms based on the ACT-R and MINERVA2 theory. We will show that these approaches can not only predict tags and items extremely well, but also reveal how these models can help in explaining the recommendation processes better than current approaches.
Je t’aime… moi non plus: reporting on the opportunities, expectations and cha...Christoph Trattner
This presentation will be a live exchange of ideas & arguments, between a representative of a start up working on agricultural information management and discovery, and a representative of academia that has recently completed his PhD and is now leading a young and promising research team.
The two presenters will focus on the case of a recommendation service that is going to be part of a web portal for organic agriculture researchers and educators (called Organic.Edunet), which will help users find relevant educational material and bibliography. They currently develop this as part of an EU-funded initiative but would both be interested to find a way to further sustain this work: the start up by including this to the bundle of services that it offers to the users of its information discovery packages, and the research team by attracting more funding to further explore recommendation technologies.
The start up representative will describe his evergoing, helpless and aimless efforts to include a research activity on recommender systems within the R&D strategy of the company, for the sakes of the good-old-PhD-times. And will explain why this failed.
The academia representative will describe the great things that his research can do to boost the performance of recommendation services in such portals. And why this does-not-work-yet-operationally because he cannot find real usage data that can prove his amazing algorithm outside what can be proven in offline lab experiments using datasets from other domains (like MovieLens and CiteULike).
Both will explain how they started working together in order to design, experimentally test, and deploy the Organic.Edunet recommendation service. And will describe their expectations from this academic-industry collaboration. Then, they will reflect on the challenges they see in such partnerships and how (if) they plan to overcome them.
Keystone summer school 2015 paolo-missier-provenancePaolo Missier
Lecture on Provenance modelling, given at the first Keystone Summer School, Malta July 2015.
With thanks to Prof. Luc Moreau for contributing some of the slide material from his own tutorial
Working with Social Media Data: Ethics & good practice around collecting, usi...Nicola Osborne
Slides from a workshop delivered for the University of Edinburgh Digital Scholarship programme, on 18th October 2017. For further information on the programme see: http://www.digital.cahss.ed.ac.uk/ or #DigScholEd. If you are interested in hosting a similar workshop, or adapting these slides please contact me: nicola.osborne@ed.ac.uk.
The Web of Data: do we actually understand what we built?Frank van Harmelen
Despite its obvious success (largest knowledge base ever built, used in practice by companies and governments alike), we actually understand very little of the structure of the Web of Data. Its formal meaning is specified in logic, but with its scale, context dependency and dynamics, the Web of Data has outgrown its traditional model-theoretic semantics.
Is the meaning of a logical statement (an edge in the graph) dependent on the cluster ("context") in which it appears? Does a more densely connected concept (node) contain more information? Is the path length between two nodes related to their semantic distance?
Properties such as clustering, connectivity and path length are not described, much less explained by model-theoretic semantics. Do such properties contribute to the meaning of a knowledge graph?
To properly understand the structure and meaning of knowledge graphs, we should no longer treat knowledge graphs as (only) a set of logical statements, but treat them properly as a graph. But how to do this is far from clear.
In this talk, I report on some of our early results on some of these questions, but I ask many more questions for which we don't have answers yet.
Introduction to Topological Data AnalysisMason Porter
Here are slides for my 3/14/21 talk on an introduction to topological data analysis.
This is the first talk in our Short Course on topological data analysis at the 2021 American Physical Society (APS) March Meeting: https://march.aps.org/program/dsoft/gsnp-short-course-introduction-to-topological-data-analysis/
Centrality in Time- Dependent NetworksMason Porter
My slides for my keynote talk at the NetSci 2018 (#NetSci2018) conference in Paris, France (June 2018). This talk will take place on Thursday 13 June in the morning.
Paper Writing in Applied Mathematics (slightly updated slides)Mason Porter
Here are my slides (which I have updated very slightly) in writing papers in applied mathematics.
There will be an accompanying oral presentation and discussion on Friday 20 April. I am recording the video for that and plan to post it along with these (or a further updated version of these) slides.
Anatoliy Gruzd and Philip Mai
Workshop presented at the TTRA Annual International Conference in Quebec City (June 20, 2017)
https://2017ttraannualinternationalconfe.sched.com/event/9yCg/social-listening-how-to-do-it-and-how-to-use-it-veille-sociale-comment-faire-et-comment-lutiliser?iframe=no&w=100%&sidebar=no&bg=no
Biocultural Variation and Obesity - EdukiteEduKite
The Unit for Biocultural Variation and Obesity (UBVO) is an interdisciplinary research unit at the University of Oxford, dedicated to understanding the complex and interwoven causes of obesity in populations across the world. This series is hosted by the Institute of Social and Cultural Anthropology, University of Oxford. Topics will be discussed are Resisting moralisation in health promotion, Anorexia, care and comfort and so on.
See More: https://bit.ly/2KvYzrE
Working with Social Media Data: Ethics & good practice around collecting, usi...Nicola Osborne
Slides from a workshop delivered for the University of Edinburgh Digital Scholarship programme, on 18th October 2017. For further information on the programme see: http://www.digital.cahss.ed.ac.uk/ or #DigScholEd. If you are interested in hosting a similar workshop, or adapting these slides please contact me: nicola.osborne@ed.ac.uk.
The Web of Data: do we actually understand what we built?Frank van Harmelen
Despite its obvious success (largest knowledge base ever built, used in practice by companies and governments alike), we actually understand very little of the structure of the Web of Data. Its formal meaning is specified in logic, but with its scale, context dependency and dynamics, the Web of Data has outgrown its traditional model-theoretic semantics.
Is the meaning of a logical statement (an edge in the graph) dependent on the cluster ("context") in which it appears? Does a more densely connected concept (node) contain more information? Is the path length between two nodes related to their semantic distance?
Properties such as clustering, connectivity and path length are not described, much less explained by model-theoretic semantics. Do such properties contribute to the meaning of a knowledge graph?
To properly understand the structure and meaning of knowledge graphs, we should no longer treat knowledge graphs as (only) a set of logical statements, but treat them properly as a graph. But how to do this is far from clear.
In this talk, I report on some of our early results on some of these questions, but I ask many more questions for which we don't have answers yet.
Introduction to Topological Data AnalysisMason Porter
Here are slides for my 3/14/21 talk on an introduction to topological data analysis.
This is the first talk in our Short Course on topological data analysis at the 2021 American Physical Society (APS) March Meeting: https://march.aps.org/program/dsoft/gsnp-short-course-introduction-to-topological-data-analysis/
Centrality in Time- Dependent NetworksMason Porter
My slides for my keynote talk at the NetSci 2018 (#NetSci2018) conference in Paris, France (June 2018). This talk will take place on Thursday 13 June in the morning.
Paper Writing in Applied Mathematics (slightly updated slides)Mason Porter
Here are my slides (which I have updated very slightly) in writing papers in applied mathematics.
There will be an accompanying oral presentation and discussion on Friday 20 April. I am recording the video for that and plan to post it along with these (or a further updated version of these) slides.
Anatoliy Gruzd and Philip Mai
Workshop presented at the TTRA Annual International Conference in Quebec City (June 20, 2017)
https://2017ttraannualinternationalconfe.sched.com/event/9yCg/social-listening-how-to-do-it-and-how-to-use-it-veille-sociale-comment-faire-et-comment-lutiliser?iframe=no&w=100%&sidebar=no&bg=no
Biocultural Variation and Obesity - EdukiteEduKite
The Unit for Biocultural Variation and Obesity (UBVO) is an interdisciplinary research unit at the University of Oxford, dedicated to understanding the complex and interwoven causes of obesity in populations across the world. This series is hosted by the Institute of Social and Cultural Anthropology, University of Oxford. Topics will be discussed are Resisting moralisation in health promotion, Anorexia, care and comfort and so on.
See More: https://bit.ly/2KvYzrE
Motivation, culture and health in a socio-ecological system in AfricaNaomi Marks
Keynote presentation by Professor Bassirou Bonfoh, Director-General, Swiss Centre for Scientific Research (CSRS), Côte d'Ivoire, at the One Health for the Real World: zoonoses, ecosystems and wellbeing symposium, London 17-18 March 2016
Presented at the 66th session of the WHO Regional Committee for Europe by Dr Claudia Stein, Director,
Information, Evidence, Research and Innovation, WHO/Europe
Stand out from the crowd by adding Evidence-Informed Public Health (EIPH) to your portfolio! Join us to learn about EIPH and resources you can use to develop these in-demand skills.
EIT FOOD @ IMPACT'17, May 31- June 1, 2017, Krakow, Poland. #IMPACTCEE #make...EIT Food
Keynote speech from two experts from EIT Food's Innovation Hub North- East:
Justyna Kulawik-Dutkowska, EIT Food
Adam Strzelecki, EIT Food
about "The Global Revolution of Food" at Impact'17, May 31- June 1, 2017, Krakow, Poland.
The project aims to satisfy the requirements of the needy organization through food donations. Food is a basic need for every living being. Daily lots of food is wasted by many people globally.“Open Food Foundation” is a project which aims to provide food to orphans or needy.
43_Program Elective course - III (Community medicine).pdfVamsi kumar
This syllabus covers the principles and applications of Community Medicine and Epidemiology. Students will gain a comprehensive understanding of community health, disease control, health promotion, and the role of medical social work. They will apply knowledge to real-world case studies, fostering skills in critical analysis, problem-solving, and ethical decision-making.
Created by: Mr. Attuluri Vamsi Kumar, Assistant Professor, Department of MLT, UIAHS, Chandigarh University, Mohali, Punjab. For more details website: https://www.mltmaster.com
In the age of internet and social media, Dr. Carl Abelardo Antonio teaches us how to evaluate online health resources so we can tell which of them is gold and which of them is junk.
This introductory presentation was given on 9 July 2019 by Lini Wollenberg. It set the scene for session 3 of the CLIFF-GRADS webinar series. This session focused on food loss and waste. Other presentations included an overview of a new FLW emissions calculator by Jan Broeze (Wageningen University & Research) as well as several prestentations by CLIFF-GRADS students of their current research.
A recording of the webinar can be found on CCAFS youtube channel: CGIAR Research Program on Climate Change, Agriculture and Food Security.
BRIF: Bioresource Research Impact Factor - Anne Cambon-Thomsen - INSERMLisette Giepmans
BioSHaRE conference July 28th, 2015, Milan - Latest tools and services for data sharing
The BRIF is a collective international initiative to build a framework for recognising and measuring the use of bioresources for research. It targets 4 main objectives that are currently ongoing:
1) fostering the assignment of a unique and persistent identifier to the bioresource by an independent international institution or body,
2) the construction of the BRIF algorithm on the basis of a number of agreed parameters for the follow-up of the use of bioresources,
3) the modification of editorial guidelines in order to coherently integrate the citation and acknowledgement of the bioresources used in scientific articles, and
4) the assessment of incentives for bioresource access and sharing policies.
Recently, members of the journal editors subgroup published the CoBRA guideline, a standardised citation scheme specific to bioresources.
Contact: Dr. Anne Cambon-Thomsen
Institut National de la Santé et de la Recherche Medicale, France
anne.cambon-thomsen@univ-tlse3.fr
Similar to Studying Online Food Consumption and Production Patterns: Recent Trends and Challenges (20)
Online food recommender systems have recently become an active field of research. While there is growing body of work investigating how online food recommender systems could potentially be designed to better meet the users’
preferences, to date less research has tried to understand how people make their food choices online, how this behavior can be modeled and even potentially changed. Why might we want to change behavior? According to the World Health
Organization around 80% of cases of heart disease, strokes and type 2 diabetes could be avoided if people would implement a healthier diet. Health-aware food recommender technologies have been touted as a valuable asset in achieving the ambitious goal of developing systems, which positively impact on the food choices people make. For example, they may help people to implement a healthier diet by suggesting healthier versions of a similar meal they typically like.
In this talk, I will present our latest research on health-aware online food recommender systems. I will show how people upload, bookmark or rate online recipes in large online food communities and how contextual factors and biases such as seasonality, temporality, social context, presentation of the recipe or gender of the recipe author have an impact on how popular online recipes are and how they are perceived. Furthermore, I will reveal to what extent these factors and
biases can be exploited to model and predict the user’s online food choices. To conclude, I will present some preliminary work aiming to exploit choice biases to nudge people towards healthier online recipes and thereby change the user’s short-term food choices.
Investigating the Healthiness of Internet-Sourced Recipes: Implications for M...Christoph Trattner
Food recommenders have the potential to positively influence the eating habits of users. To achieve this, however, we need to understand how healthy recommendations are and the factors which influence this. Focusing on two approaches from the literature (single item and daily meal plan recommendation) and utilizing a large Internet sourced dataset from Allrecipes.com, we show how algorithmic solutions relate to the healthiness of the underlying recipe collection. First, we analyze the healthiness of Allrecipes.com recipes using nutritional standards from the World Health Organisation and the United Kingdom Food Standards Agency. Second, we investigate user interaction patterns and how these relate to the healthiness of recipes. Third, we experiment with both recommendation approaches. Our results indicate that overall the recipes in the collection are quite unhealthy, but this varies across categories on the website. Users in general tend to interact most often with the least healthy recipes. Recommender algorithms tend to score popular items highly and thus on average promote unhealthy items. This can be tempered, however, with simple post-filtering approaches, which we show by experiment are better suited to some algorithms than others. Similarly, we show that the generation of meal plans can dramatically increase the number of healthy options open to users. One of the main findings is, nevertheless, that the utility of both approaches is strongly restricted by the recipe collection. Based on our findings we draw conclusions how researchers should attempt to make food recommendation systems promote healthy nutrition.
Understanding the Impact of Weather for POI RecommendationsChristoph Trattner
POI recommender systems for location-based social network services, such as Foursquare or Yelp, have gained tremendous popularity in the past few years. Much work has been dedicated into improving recommendation services in such systems by integrating different features that are assumed to have an impact on people's preferences for POIs, such as time and geolocation. Yet, little attention has been paid to the impact of weather on the users' final decision to visit a recommended POI. In this paper we contribute to this area of research by presenting the first results of a study that aims to recommend POIs based on weather data. To this end, we extend the state-of-the-art Rank-GeoFM POI recommender algorithm with additional weather-related features, such as temperature, cloud cover, humidity and precipitation intensity. We show that using weather data not only significantly increases the recommendation accuracy in comparison to the original algorithm, but also outperforms its time-based variant. Furthermore, we present the magnitude of impact of each feature on the recommendation quality, showing the need to study the weather context in more detail in the light of POI recommendation systems.
Towards a Big Data Recommender Engine for Online and Offline MarketplacesChristoph Trattner
Recommender systems aim at helping users to find relevant information in an overloaded information space.
Although there are well known methods (Content-based, Collaborative Filtering, Matrix Factorization) and libraries to implement, evaluate and extend recommenders (Apache Mahout, Graphlab, MyMediaLite, among others), the deployment of a real-time recommender from scratch which considers a combination of algorithms and various data sources (e.g., social, transactional, and location) remains unsolved.
In this talk, we report on the challenges towards such a recommender systems in the context of online of offline marketplaces. In particular, we describe our solution in terms of the requirements, the data model and algorithms that allows modularity and extensibility, as well as the system architecture to facilitate the scaling of our approach to big data for online and offline marketplaces.
Social Computing in the area of Big Data at the Know-Center Austria's leading...Christoph Trattner
Nowadays, social networks and media, such as Facebook, Twitter & Co, affect our communication and our exchange of knowledge more than ever. But which additional benefits can offer social media apart from easy interaction with friends and how can they be used to create additional value for companies and institutions? These are the questions that the area Social Computing at Know-Center addresses in detail.
In this talk we will give a brief overview of industry and non-industry related research projects which we have been involved in recently with my group, Social Computing at the Know-Center, in the context of Big Data and social media. In particular, the talk will highlight specific research project outcomes and work-in-progress that make use of social media data to help people to explore the vastly growing overloaded information space more efficiently.
Recommending Tags with a Model of Human CategorizationChristoph Trattner
Social tagging involves complex processes of human categorization that have been the topic of much research in the cognitive sciences. In this paper we present a recommender approach for social tags whose principles are derived from some of the more prominent and empirically well-founded models from this research tradition. The basic architecture is a simple three-layers connectionist model. The input layer encodes patterns of semantic features of a user-specific re- source, which are either latent topics elicited through Latent Dirichlet Allocation (LDA) or available external categories. The hidden layer categorizes the resource by matching the encoded pattern against already learned exemplar patterns. The latter are composed of unique feature patterns and associated tag distributions. Finally, the output layer samples tags from the associated tag distributions to verbalize the preceding categorization process. We have evaluated this approach on a real-world folksonomy gathered from Wikipedia bookmarks in Delicious. In the experiment our approach outperformed LDA, a well-established algorithm. We at- tribute this to the fact that our approach processes seman- tic information (either latent topics or external categories) across the three different layers, and this substantially enhances the recommendation performance. With this paper, we demonstrate that a theoretically guided design of algorithms not only holds potential for improving existing recommendation mechanisms, but it also allows us to derive more generalizable insights about how human information interaction on the Web is determined by both semantic and verbal processes.
Evaluating Tag-Based Information Access in Image CollectionsChristoph Trattner
The availability of social tags has greatly enhanced access to information.
Tag clouds have emerged as a new “social” way to find
and visualize information, providing both one-click access to information
and a snapshot of the “aboutness” of a tagged collection.
A range of research projects explored and compared different tag
artifacts for information access ranging from regular tag clouds to
tag hierarchies. At the same time, there is a lack of user studies that
compare the effectiveness of different types of tag-based browsing
interfaces from the users point of view. This paper contributes to
the research on tag-based information access by presenting a controlled
user study that compared three types of tag-based interfaces
on two recognized types of search tasks – lookup and exploratory
search. Our results demonstrate that tag-based browsing interfaces
significantly outperform traditional search interfaces in both performance
and user satisfaction. At the same time, the differences
between the two types of tag-based browsing interfaces explored in
our study are not as clear.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
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Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
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This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
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Quantitative data Analysis
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Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
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Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
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Sum with different modes (reduce)
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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
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
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.
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
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.
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’
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. 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
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. 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
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.
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.
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. 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
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. 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
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. 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.