Food is essential for human life and it is fundamental to the human experience. Food-related study may support multifarious applications and services, such as guiding human behavior, improving human health, and understanding the culinary culture. With the rapid development of social networks, mobile networks, and Internet of Things (IoT), people commonly upload, share, and record food images, recipes, cooking videos, and food diaries, leading to large-scale food data. Large-scale food data offers rich knowledge about food and can help tackle many central issues of human society. Therefore, it is time to group several disparate issues related to food computing. Food computing acquires and analyzes heterogenous food data from different sources for perception, recognition, retrieval, recommendation, and monitoring of food. In food computing, computational approaches are applied to address food-related issues in medicine, biology, gastronomy, and agronomy. Both large-scale food data and recent breakthroughs in computer science are transforming the way we analyze food data. Therefore, a series of works has been conducted in the food area, targeting different food-oriented tasks and applications. However, there are very few systematic reviews that shape this area well and provide a comprehensive and in-depth summary of current efforts or detail open problems in this area. In this article, we formalize food computing and present such a comprehensive overview of various emerging concepts, methods, and tasks. We summarize key challenges and future directions ahead for food computing. This is the first comprehensive survey that targets the study of computing technology for the food area and also offers a collection of research studies and technologies to benefit researchers and practitioners working in different food-related fields.

A Survey on Food Computing

Cuisine is a style of cooking and usually associated with a specific geographic region. Recipes from different cuisines shared on the web are an indicator of culinary cultures in different countries. Therefore, analysis of these recipes can lead to deep understanding of food from the cultural perspective. In this paper, we perform the first cross-region recipe analysis by jointly using the recipe ingredients, food images, and attributes such as the cuisine and course (e.g., main dish and dessert). For that solution, we propose a culinary culture analysis framework to discover the topics of ingredient bases and visualize them to enable various applications. We first propose a probabilistic topic model to discover cuisine-course specific topics. The manifold ranking method is then utilized to incorporate deep visual features to retrieve food images for topic visualization. At last, we applied the topic modeling and visualization method for three applications: 1) multimodal cuisine summarization with both recipe ingredients and images, 2) cuisine-course pattern analysis including topic-specific cuisine distribution and cuisine-specific course distribution of topics, and 3) cuisine recommendation for both cuisine-oriented and ingredient-oriented queries. Through these three applications, we can analyze the culinary cultures at both macro and micro levels. We conduct the experiment on a recipe database Yummly-66K with 66,615 recipes from 10 cuisines in Yummly. Qualitative and quantitative evaluation results have validated the effectiveness of topic modeling and visualization, and demonstrated the advantage of the framework in utilizing rich recipe information to analyze and interpret the culinary cultures from different regions.

You Are What You Eat: Exploring Rich Recipe Information for Cross-Region Food Analysis

Many mobile apps of social Internet of Things (sIOT) systems can help us record and share daily events, such as health and sport events. In fact, healthy diet recognition is an important and challenging problem in dish health assessment. Via the collection and monitoring of data pertaining to our daily diet, we can work in collaborative ways to achieve dish image annotation based on sIOT systems to enhance deep features. To this end, this article proposes a deep feature and attention mechanism-based method for dish health assessment, which aims to apply a hand-deep local–global net (HDLGN) for dish image recognition. Then, food taste is used as health guidance for people who want to lose weight or follow doctors’ advice. First, the local attention mechanism is introduced to identify key areas of the dish image. Second, ingredient and handcrafted color features are extracted to learn deep features. Subsequently, we combine local and global attention mechanisms to return the dish taste as the recognition result. Finally, experiments show that our proposed method can effectively improve the accuracy of taste recognition. 

The Deep Features and Attention Mechanism-Based Method to Dish Healthcare Under Social IoT Systems: An Empirical Study With a Hand-Deep Local–Global Net

Automatic detection of relationships between banking operations using machine learning

In this paper, we explore the problem of identifying substitute relationship between food pairs from real-world food consumption data as the first step towards the healthier food recommendation. Our method is inspired by the distributional hypothesis in linguistics. Specifically, we assume that foods that are consumed in similar contexts are more likely to be similar dietarily. For example, a turkey sandwich can be considered a suitable substitute for a chicken sandwich if both tend to be consumed with french fries and salad. To evaluate our method, we constructed a real-world food consumption dataset from MyFitnessPal's public food diary entries and obtained ground-truth human judgements of food substitutes from a crowdsourcing service. The experiment results suggest the effectiveness of the method in identifying suitable substitutes.

Extracting Food Substitutes From Food Diary via Distributional Similarity

It is well known that in a balanced diet, eating the right amount of calories and nutrients to maintain a healthy weight is important for one's physical wellness and health. Thus, understanding demographic and behavior patterns of food consumption is a topic that several researchers in public health have long pursued. In this paper, we study how perceived food tastes, which are known to affect palatability of foods, are related to the dynamics and nature of population-wide dietary preferences and patterns over demographics, time, and location. In contrast to previous studies that have been clinical in nature based on small samples of participants via treatment data, our study offers a more ``big data''-style approach by leveraging a massive collection of food items and entries from the MyFitnessPal user base. Despite its differences from traditional research, our findings actually validate some previous studies that correlate food taste with certain population groups or public health patterns. In addition, we are able to extend research into previously unexploited directions.

Analyzing Taste Preferences From Crowdsourced Food Entries

We study the problem of how to match a formally structured restaurant menu item to a large database of less structured food items that has been collected via crowd-sourcing. At first glance, this problem scenario looks like a typical text matching problem that might possibly be solved with existing text similarity learning approaches. However, due to the unique nature of our scenario and the need for scalability, our problem imposes certain restrictions on possible machine learning approaches that we can employ. We propose a novel, practical, and scalable machine learning solution architecture, consisting of two major steps. First we use a query generation approach, based on a Markov Decision Process algorithm, to reduce the time complexity of searching for matching candidates. That is then followed by a re-ranking step, using deep learning techniques, to meet our required matching quality goals. It is important to note that our proposed solution architecture has already been deployed in a real application system serving tens of millions of users, and shows great potential for practical cases of user-entered text to structured text matching, especially when scalability is crucial.

Matching Restaurant Menus to Crowdsourced Food Data: A Scalable Machine Learning Approach

A method of operating a health tracking system is disclosed. The method comprises: receiving a first data record comprising at least a first descriptive string regarding a consumable item, the first descriptive string having at least one word thereof incorrectly spelled; generating a vector using the first descriptive string using a machine learning model; identifying a second descriptive string which corresponds to the consumable item and which has a correct spelling of the at least one incorrectly spelled word by applying the machine learning model to the generated vector; calculating a confidence factor regarding the identified second descriptive string using the machine learning model; and when it is determined that the confidence factor exceeds a predetermined threshold, (i) modifying the first data record by replacing the first descriptive string with the second descriptive string, and (ii) storing the modified first data record in the database.

Context and domain sensitive spelling correction in a database

Due to the emergence of several nutrition-related mobile applications and websites in recent years, as well as the massive amount of crowd-sourced nutrition data, searching and finding relevant results has become increasingly difficult for users. This problem becomes even more challenging when dealing with crowd-sourced food names that are noisy and not well-structured. Because food names are short in length, it is difficult to incorporate existing methods to achieve an optimal matching quality. Despite several recent studies on nutrition data, these challenges remain. In this paper, we propose a novel learning-to-rank framework for crowd-sourced food names that has significant real-world applications, including food search and food recommendations. In particular, we propose a deep learning based, multi-modal learning-to-rank model that leverages the text describing a food name and the numerical values that represent its nutritional information. To this end, we also introduce a novel type of loss-function, which extends standard triplets hinge loss function into a multi-modal scenario. The proposed model is flexible and supports various data types as well as an arbitrary number of modalities. The effectiveness of our proposed model is demonstrated through several experiments on real-data, consisting of more than six million instances.

A deep multi-modal pairwise ranking model for user generated food data

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Papers

Analyzing Taste Preferences From Crowdsourced Food Entries

Matching Restaurant Menus to Crowdsourced Food Data: A Scalable Machine Learning Approach

Context and domain sensitive spelling correction in a database

A deep multi-modal pairwise ranking model for user generated food data