42 research outputs found
A neural network system for transformation of regional cuisine style
We propose a novel system which can transform a recipe into any selected
regional style (e.g., Japanese, Mediterranean, or Italian). This system has two
characteristics. First the system can identify the degree of regional cuisine
style mixture of any selected recipe and visualize such regional cuisine style
mixtures using barycentric Newton diagrams. Second, the system can suggest
ingredient substitutions through an extended word2vec model, such that a recipe
becomes more authentic for any selected regional cuisine style. Drawing on a
large number of recipes from Yummly, an example shows how the proposed system
can transform a traditional Japanese recipe, Sukiyaki, into French style
Introduction to GEPHI
Gephi is a visualization and exploration software for graphs and networks. Think Photoshop, but for graph data. This session will provide an overview of the software, its features, and resources for further study. Gephi is open-source, free to download, and runs on Windows, Mac OS X, and Linux.
The presentation slides are available by clicking the Download button on the right. The video and audio files of this workshop are listed as the additional files below and are available for download
Insights from Machine-Learned Diet Success Prediction
To support people trying to lose weight and stay healthy, more and more
fitness apps have sprung up including the ability to track both calories intake
and expenditure. Users of such apps are part of a wider ``quantified self''
movement and many opt-in to publicly share their logged data. In this paper, we
use public food diaries of more than 4,000 long-term active MyFitnessPal users
to study the characteristics of a (un-)successful diet. Concretely, we train a
machine learning model to predict repeatedly being over or under self-set daily
calories goals and then look at which features contribute to the model's
prediction. Our findings include both expected results, such as the token
``mcdonalds'' or the category ``dessert'' being indicative for being over the
calories goal, but also less obvious ones such as the difference between pork
and poultry concerning dieting success, or the use of the ``quick added
calories'' functionality being indicative of over-shooting calorie-wise. This
study also hints at the feasibility of using such data for more in-depth data
mining, e.g., looking at the interaction between consumed foods such as mixing
protein- and carbohydrate-rich foods. To the best of our knowledge, this is the
first systematic study of public food diaries.Comment: Preprint of an article appearing at the Pacific Symposium on
Biocomputing (PSB) 2016 in the Social Media Mining for Public Health
Monitoring and Surveillance trac
Uncovering the Wider Structure of Extreme Right Communities Spanning Popular Online Networks
Recent years have seen increased interest in the online presence of extreme
right groups. Although originally composed of dedicated websites, the online
extreme right milieu now spans multiple networks, including popular social
media platforms such as Twitter, Facebook and YouTube. Ideally therefore, any
contemporary analysis of online extreme right activity requires the
consideration of multiple data sources, rather than being restricted to a
single platform. We investigate the potential for Twitter to act as a gateway
to communities within the wider online network of the extreme right, given its
facility for the dissemination of content. A strategy for representing
heterogeneous network data with a single homogeneous network for the purpose of
community detection is presented, where these inherently dynamic communities
are tracked over time. We use this strategy to discover and analyze persistent
English and German language extreme right communities.Comment: 10 pages, 11 figures. Due to use of "sigchi" template, minor changes
were made to ensure 10 page limit was not exceeded. Minor clarifications in
Introduction, Data and Methodology section
Uncovering the wider structure of extreme right communities spanning popular online networks
AbstractRecent years have seen increased interest in the online presence of extreme right groups. Although originally composed of dedicated websites, the online extreme right milieu now spans multiple networks, including popular social media platforms such as Twitter, Facebook and YouTube. Ideally therefore, any contemporary analysis of online extreme right activity requires the consideration of multiple data sources, rather than being restricted to a single platform.We investigate the potential for Twitter to act as one possible gateway to communities within the wider online network of the extreme right, given its facility for the dissemination of content. A strategy for representing heterogeneous network data with a single homogeneous network for the purpose of community detection is presented, where these inherently dynamic communities are tracked over time. We use this strategy to discover and analyze persistent English and German language extreme right communities.Authored by Derek O’Callaghan, Derek Greene, Maura Conway, Joe Carthy and Padraig Cunningham
Hierarchical Attention Network for Visually-aware Food Recommendation
Food recommender systems play an important role in assisting users to
identify the desired food to eat. Deciding what food to eat is a complex and
multi-faceted process, which is influenced by many factors such as the
ingredients, appearance of the recipe, the user's personal preference on food,
and various contexts like what had been eaten in the past meals. In this work,
we formulate the food recommendation problem as predicting user preference on
recipes based on three key factors that determine a user's choice on food,
namely, 1) the user's (and other users') history; 2) the ingredients of a
recipe; and 3) the descriptive image of a recipe. To address this challenging
problem, we develop a dedicated neural network based solution Hierarchical
Attention based Food Recommendation (HAFR) which is capable of: 1) capturing
the collaborative filtering effect like what similar users tend to eat; 2)
inferring a user's preference at the ingredient level; and 3) learning user
preference from the recipe's visual images. To evaluate our proposed method, we
construct a large-scale dataset consisting of millions of ratings from
AllRecipes.com. Extensive experiments show that our method outperforms several
competing recommender solutions like Factorization Machine and Visual Bayesian
Personalized Ranking with an average improvement of 12%, offering promising
results in predicting user preference for food. Codes and dataset will be
released upon acceptance
Bargaining agents based system for automatic classification of potential allergens in recipes
The automatic recipe recommendation which take into account the dietary restrictions of users (such as allergies or intolerances) is a complex and open problem. Some of the limitations of the problem is the lack of food databases correctly labeled with its potential allergens and non-unification of this information by companies in the food sector. In the absence of an appropriate solution, people affected by food restrictions cannot use recommender systems, because this recommend them inappropriate recipes. In order to resolve this situation, in this article we propose a solution based on a collaborative multi-agent system, using negotiation and machine learning techniques, is able to detect and label potential allergens in recipes. The proposed system is being employed in receteame.com, a recipe recommendation system which includes persuasive technologies, which are interactive technologies aimed at changing users’ attitudes or behaviors through persuasion and social influence, and social information to improve the recommendations