A Food Recommender System in Academic Environments Based on Machine Learning Models

Background: People's health depends on the use of proper diet as an important factor. Today, with the increasing mechanization of people's lives, proper eating habits and behaviors are neglected. On the other hand, food recommendations in the field of health have also tried to deal with this issue. But with the introduction of the Western nutrition style and the advancement of Western chemical medicine, many issues have emerged in the field of disease treatment and nutrition. Recent advances in technology and the use of artificial intelligence methods in information systems have led to the creation of recommender systems in order to improve people's health. Methods: A hybrid recommender system including, collaborative filtering, content-based, and knowledge-based models was used. Machine learning models such as Decision Tree, k-Nearest Neighbors (kNN), AdaBoost, and Bagging were investigated in the field of food recommender systems on 2519 students in the nutrition management system of a university. Student information including profile information for basal metabolic rate, student reservation records, and selected diet type is received online. Among the 15 features collected and after consulting nutrition experts, the most effective features are selected through feature engineering. Using machine learning models based on energy indicators and food selection history by students, food from the university menu is recommended to students. Results: The AdaBoost model has the highest performance in terms of accuracy with a rate of 73.70 percent. Conclusion: Considering the importance of diet in people's health, recommender systems are effective in obtaining useful information from a huge amount of data. Keywords: Recommender system, Food behavior and habits, Machine learning, Classificatio

The Users' Perspective on the Privacy-Utility Trade-offs in Health Recommender Systems

Privacy is a major good for users of personalized services such as recommender systems. When applied to the field of health informatics, privacy concerns of users may be amplified, but the possible utility of such services is also high. Despite availability of technologies such as k-anonymity, differential privacy, privacy-aware recommendation, and personalized privacy trade-offs, little research has been conducted on the users' willingness to share health data for usage in such systems. In two conjoint-decision studies (sample size n=521), we investigate importance and utility of privacy-preserving techniques related to sharing of personal health data for k-anonymity and differential privacy. Users were asked to pick a preferred sharing scenario depending on the recipient of the data, the benefit of sharing data, the type of data, and the parameterized privacy. Users disagreed with sharing data for commercial purposes regarding mental illnesses and with high de-anonymization risks but showed little concern when data is used for scientific purposes and is related to physical illnesses. Suggestions for health recommender system development are derived from the findings.Comment: 32 pages, 12 figure

Automated Recommendation of Healthy, Personalised Meal Plans

Poor health due to a lack of understanding of nutrition is a major problem in the modern world, one which could potentially be addressed via the use of recommender systems. In this demo we present a system to generate meal plans for users which they will not only like, based on their taste preferences, but will also conform to daily nutritional guidelines. The interface allows the selection of recipes for breakfast, lunch and dinner and can automatically complete a daily meal plan or can generate entire plans itself

Health-aware Food Planner: A Personalized Recipe Generation Approach Based on GPT-2

What to eat today? With the flourish of Internet, more and more people nowadays are inclined to find an answer to this most problematic question online. The recent explosion of food networks; however, produces large volumes of recipes, making it even harder to make an informed decision. This yields the need for advanced decision-making algorithms and efficient recommendation systems. Conventional recommender systems are not feasible anymore as food is a complicated feature that presents unique challenges and is less studied. For example, it can be one of the main reasons for obesity and many other chronic diseases. Food recommender system has the potential to urge users to change their eating behaviors by adding a healthiness component as another factor in the recommendation procedure. Text generation, a hot area in machine learning, can be used as a part of a food recommender system to explore new recipes. However, existing works do not include the factors of users’ preferences, nutritional needs, and knowledge of the ingredients. In this work, we tackle this issue by proposing a new task of healthy and personalized recipe generation given only a few ingredients. We also suggest personalizing the ingredient list by integrating the user profile extracted from the previous history. Specifically, our model consists of three main components: 1) completing the given initial ingredient list by predicting the most relevant, healthy, and personalized ingredients, 2) fine-tuning GPT-2 model to generate a new recipe given the ingredients, 3) finding and recommending the top similar recipes to the generated one. In contrast to other recipe generation models, we expand the final output to be the generated recipes in addition to the top-k similar recipes from the dataset. All the proposed solutions in this work have been evaluated separately to compare their evaluation against their related works using suitable metrics. In addition to that, we did further analysis to study the hyperparameters and design options. By doing so, we intend to show our model’s ability to recommend new yet logical recipes that balance the preferences with the healthiness