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.
Concerning the analysis of food consumption through user-generated data, some studies have taken a public health approach and looked at regional or temporal patterns in food consumption. West al @cite_4 used web search and click-through data to study seasonal changes in the recipes people search for. Server logs from a recipe site were used in @cite_15 @cite_6 to study regional differences in food preferences. Their angle is less health-centric and more culture- or preference-centric with a focus on whether ingredients or regional influences dominate the recipe choice. Using the ingredients of recipes, rather than regional or cultural knowledge, has been explored in @cite_11 for the purpose of recipe recommendation.
@cite_15 @cite_4 @cite_6 @cite_11
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Food and nutrition occupy an increasingly prevalent space on the web, and dishes and recipes shared online provide an invaluable mirror into culinary cultures and attitudes around the world. More specifically, ingredients, flavors, and nutrition information become strong signals of the taste preferences of individuals and civilizations. However, there is little understanding of these palate varieties. In this paper, we present a large-scale study of recipes published on the web and their content, aiming to understand cuisines and culinary habits around the world. Using a database of more than 157K recipes from over 200 different cuisines, we analyze ingredients, flavors, and nutritional values which distinguish dishes from different regions, and use this knowledge to assess the predictability of recipes from different cuisines. We then use country health statistics to understand the relation between these factors and health indicators of different nations, such as obesity, diabetes, migration, and health expenditure. Our results confirm the strong effects of geographical and cultural similarities on recipes, health indicators, and culinary preferences across the globe. Since food is one of the central elements of all human beings, a high interest exists in exploring temporal and spatial food and dietary patterns of humans. Predominantly, data for such investigations stem from consumer panels which continuously capture food consumption patterns from individuals and households. In this work we leverage data from a large online recipe platform which is frequently used in the German speaking regions in Europe and explore (i) the association between geographic proximity and shared food preferences and (ii) to what extent temporal information helps to predict the food preferences of users. Our results reveal that online food preferences of geographically closer regions are more similar than those of distant ones and show that specific types of ingredients are more popular on specific days of the week. The observed patterns can successfully be mapped to known real-world patterns which suggests that existing methods for the investigation of dietary and food patterns (e.g., consumer panels) may benefit from incorporating the vast amount of data generated by users browsing recipes on the Web. Human beings have developed a diverse food culture. Many factors like ingredients, visual appearance, courses (e.g., breakfast and lunch), flavor and geographical regions affect our food perception and choice. In this work, we focus on multi-dimensional food analysis based on these food factors to benefit various applications like summary and recommendation. For that solution, we propose a delicious recipe analysis framework to incorporate various types of continuous and discrete attribute features and multi-modal information from recipes. First, we develop a Multi-Attribute Theme Modeling (MATM) method, which can incorporate arbitrary types of attribute features to jointly model them and the textual content. We then utilize a multi-modal embedding method to build the correlation between the learned textual theme features from MATM and visual features from the deep learning network. By learning attribute-theme relations and multi-modal correlation, we are able to fulfill different applications, including (1) flavor analysis and comparison for better understanding the flavor patterns from different dimensions, such as the region and course, (2) region-oriented multi-dimensional food summary with both multi-modal and multi-attribute information and (3) multi-attribute oriented recipe recommendation. Furthermore, our proposed framework is flexible and enables easy incorporation of arbitrary types of attributes and modalities. Qualitative and quantitative evaluation results have validated the effectiveness of the proposed method and framework on the collected Yummly dataset. Food is a central element of humans’ life, and food preferences are amongst others manifestations of social, cultural and economic forces that influence the way we view, prepare and consume food. Historically, data for studies of food preferences stems from consumer panels which continuously capture food consumption and preference patterns from individuals and households. In this work we look at a new source of data, i.e., server log data from a large recipe platform on the World Wide Web, and explore its usefulness for understanding online food preferences. The main findings of this work are: (i) recipe preferences are partly driven by ingredients, (ii) recipe preference distributions exhibit more regional differences than ingredient preference distributions, and (iii) weekday preferences are clearly distinct from weekend preferences.