Alternative Ingredient Recommendation: A Co-occurrence and Ingredient Category Importance Based Approach

As many people will refer to a recipe when cooking, there are several recipe-sharing websites that include lots of recipes and make recipes easier to access than before. However, there is often the case that we could not get all the ingredients listed on the recipe. Prior research on alternative ingredient substitution has built a recommendation system considering the suitability of a recommended ingredient with the remained ingredients. In this paper, in addition to suitability, we also take the diversity of the ingredient categories and the novelty of new combination of ingredients into account. Besides, we combine suitability with novelty as an index, to see whether our method could help find out a new combination of ingredients that is possibly to be a new dish. Our evaluation results show that our proposed method attains a comparable or even better performance on each perspective

The Cholesterol Factor: Balancing Accuracy and Health in Recipe Recommendation Through a Nutrient-Specific Metric

Whereas many food recommender systems optimize for users’ preferences, health is another but often overlooked objective. This paper aims to recommend relevant recipes that avoid nutrients that contribute to high levels of cholesterol, such as saturated fat and sugar. We introduce a novel metric called ‘The Cholesterol Factor’, based on nutritional guidelines from the Norwegian Directorate of Health, that can balance accuracy and health through linear re-weighting in post-filtering. We tested popular recommender approaches by evaluating a recipe dataset from AllRecipes.com, in which a CF-based SVD method outperformed content-based and hybrid methods. Although we found that increasing the healthiness of a recommended recipe set came at the cost of Precision and Recall metrics, only putting little weight (10-15%) on our Cholesterol Factor can significantly improve the healthiness of a recommendation set with minimal accuracy losses.publishedVersio

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