This paper presents an analysis of social experiences around wine consumption through the lens of Vivino, a social network for wine enthusiasts with over 26 million active users worldwide. We compare users' perceptions of various wine types and regional styles across both New and Old World wines, examining wines across price ranges, vintages, regions, variet als, and blends. We find that ratings provided by Vivino users are not biased by the cost of the wines. Then, we study how wine characteristics, language in wine reviews, and the distribution of wine ratings can be combined to develop prediction and recommendation models. More specifically, we model user behavior to develop three models: (1) a regression model for predicting wine ratings, (2) a classifier for determining user review preferences, and (3) a recommender system for suggesting wines based on history of ratings and reviews.
Another line of work introduces models for predicting the cuisine and the geographical origin of a dish from its recipe @cite_21 @cite_17 , collecting data from wiki-like pages, rather than social networks. These mostly aim to develop supervised learning models to predict the cuisine, via multi-class learning. This is similar to one of the models we propose, although we also use a regression based one to determine wine ratings in real-value terms. Moreover, the datasets they use are significantly smaller than ours, i.e., thousands vs millions of entries. We also find that confusion matrices presented in previous work show that the most commonly misclassified cuisines are from the same regions of the world, thus, the presumable source of their ingredients is from the same locations. In our work, we aim to discover if there was any detectable confusion between Old and New World wines; besides looking at prediction models, we also consider models for recommendation. These allow us to learn more about users, as they introduce users to new genres, in our case, wine types, which they might have not previously considered.
@cite_21 @cite_17
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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. 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. In this paper, we introduce a new and challenging large-scale food image dataset called "ChineseFoodNet", which aims to automatically recognizing pictured Chinese dishes. Most of the existing food image datasets collected food images either from recipe pictures or selfie. In our dataset, images of each food category of our dataset consists of not only web recipe and menu pictures but photos taken from real dishes, recipe and menu as well. ChineseFoodNet contains over 180,000 food photos of 208 categories, with each category covering a large variations in presentations of same Chinese food. We present our efforts to build this large-scale image dataset, including food category selection, data collection, and data clean and label, in particular how to use machine learning methods to reduce manual labeling work that is an expensive process. We share a detailed benchmark of several state-of-the-art deep convolutional neural networks (CNNs) on ChineseFoodNet. We further propose a novel two-step data fusion approach referred as "TastyNet", which combines prediction results from different CNNs with voting method. Our proposed approach achieves top-1 accuracies of 81.43 on the validation set and 81.55 on the test set, respectively. The latest dataset is public available for research and can be achieved at this https URL. We present a system which can recognize the contents of your meal from a single image, and then predict its nutritional contents, such as calories. The simplest version assumes that the user is eating at a restaurant for which we know the menu. In this case, we can collect images offline to train a multi-label classifier. At run time, we apply the classifier (running on your phone) to predict which foods are present in your meal, and we lookup the corresponding nutritional facts. We apply this method to a new dataset of images from 23 different restaurants, using a CNN-based classifier, significantly outperforming previous work. The more challenging setting works outside of restaurants. In this case, we need to estimate the size of the foods, as well as their labels. This requires solving segmentation and depth volume estimation from a single image. We present CNN-based approaches to these problems, with promising preliminary results.