Estimating the Healthiness of Internet Recipes: A cross sectional study

A government's response to increasing incidence of lifestyle-related illnesses, such as obesity, has been to encourage people to cook for themselves. The healthiness of home cooking will, nevertheless, depend on what people cook and how they cook it. In this article, one common source of cooking inspiration-Internet-sourced recipes-is investigated in depth. The energy and macronutrient content of 5,237 main meal recipes from the food website Allrecipes. com are compared with those of 100 main meal recipes from five bestselling cookery books from popular celebrity chefs and 100 ready meals from the three leading UK supermarkets. The comparison is made using nutritional guidelines published by the World Health Organization and the UK Food Standards Agency. The main conclusions drawn from our analyses are that Internet recipes sourced from Allrecipes. com are less healthy than TV chef recipes and ready meals from leading UK supermarkets. Only 6 out of 5,237 Internet recipes fully complied with the WHO recommendations. Internet recipes were more likely to meet the WHO guidelines for protein than other classes of meal (10.88 v 7% (TV), p < 0.01; 10.86 v 9% (ready), p < 0.01). However, the Internet recipes were less likely to meet the criteria for fat (14.28 v 24 (TV) v 37% (ready); p < 0.01), saturated fat (25.05 v 33 (TV) v 34% (ready); p < 0.01), and fiber (compared to ready meals 16.50 v 56%; p < 0.01). More Internet recipes met the criteria for sodium density than ready meals (19.63 v 4%; p < 0.01), but fewer than the TV chef meals (19.32 v 36%; p < 0.01). For sugar, no differences between Internet recipes and TV chef recipes were observed (81.1 v 81% (TV); p = 0.86), although Internet recipes were less likely to meet the sugar criteria than ready meals (81.1 v 83% (ready); p < 0.01). Repeating the analyses for each year of available data shows that the results are very stable over time

Hierarchical Attention Network for Visually-aware Food Recommendation

Food recommender systems play an important role in assisting users to identify the desired food to eat. Deciding what food to eat is a complex and multi-faceted process, which is influenced by many factors such as the ingredients, appearance of the recipe, the user's personal preference on food, and various contexts like what had been eaten in the past meals. In this work, we formulate the food recommendation problem as predicting user preference on recipes based on three key factors that determine a user's choice on food, namely, 1) the user's (and other users') history; 2) the ingredients of a recipe; and 3) the descriptive image of a recipe. To address this challenging problem, we develop a dedicated neural network based solution Hierarchical Attention based Food Recommendation (HAFR) which is capable of: 1) capturing the collaborative filtering effect like what similar users tend to eat; 2) inferring a user's preference at the ingredient level; and 3) learning user preference from the recipe's visual images. To evaluate our proposed method, we construct a large-scale dataset consisting of millions of ratings from AllRecipes.com. Extensive experiments show that our method outperforms several competing recommender solutions like Factorization Machine and Visual Bayesian Personalized Ranking with an average improvement of 12%, offering promising results in predicting user preference for food. Codes and dataset will be released upon acceptance

Recipe popularity prediction in Finnish social media by machine learning models

Abstract. In recent times, the internet has emerged as a primary source of cooking inspiration, eating experiences and food social gathering with a majority of individuals turning to online recipes, surpassing the usage of traditional cookbooks. However, there is a growing concern about the healthiness of online recipes. This thesis focuses on unraveling the determinants of online recipe popularity by analyzing a dataset comprising more than 5000 recipes from Valio, one of Finland’s leading corporations. Valio’s website serves as a representation of diverse cooking preferences among users in Finland. Through examination of recipe attributes such as nutritional content (energy, fat, salt, etc.), food preparation complexity (cooking time, number of steps, required ingredients, etc.), and user engagement (the number of comments, ratings, sentiment of comments, etc.), we aim to pinpoint the critical elements influencing the popularity of online recipes. Our predictive model-Logistic Regression (classification accuracy and F1 score are 0.93 and 0.9 respectively)- substantiates the existence of pertinent recipe characteristics that significantly influence their rates. The dataset we employ is notably influenced by user engagement features, particularly the number of received ratings and comments. In other words, recipes that garner more attention in terms of comments and ratings tend to have higher rates values (i.e., more popular). Additionally, our findings reveal that a substantial portion of Valio’s recipes falls within the medium health Food Standards Agency (FSA) score range, and intriguingly, recipes deemed less healthy tend to receive higher average ratings from users. This study advances our comprehension of the factors contributing to the popularity of online recipes, providing valuable insights into contemporary cooking preferences in Finland as well as guiding future dietary policy shift.Reseptin suosion ennustaminen suomalaisessa sosiaalisessa mediassa koneoppimismalleilla. Tiivistelmä. Internet on viime aikoina noussut ensisijaiseksi inspiraation lähteeksi ruoanlaitossa, ja suurin osa ihmisistä on siirtynyt käyttämään verkkoreseptejä perinteisten keittokirjojen sijaan. Huoli verkkoreseptien terveellisyydestä on kuitenkin kasvava. Tämä opinnäytetyö keskittyy verkkoreseptien suosioon vaikuttavien tekijöiden selvittämiseen analysoimalla yli 5000 reseptistä koostuvaa aineistoa Suomen johtavalta maitotuoteyritykseltä, Valiolta. Valion verkkosivujen reseptit edustavat monipuolisesti suomalaisten käyttäjien ruoanlaittotottumuksia. Tarkastelemalla reseptin ominaisuuksia, kuten ravintoarvoa (energia, rasva, suola, jne.), valmistuksen monimutkaisuutta (keittoaika, vaiheiden määrä, tarvittavat ainesosat, jne.) ja käyttäjien sitoutumista (kommenttien määrä, arviot, kommenttien mieliala, jne.), pyrimme paikantamaan kriittiset tekijät, jotka vaikuttavat verkkoreseptien suosioon. Ennustava mallimme — Logistic Regression (luokituksen tarkkuus 0,93 ja F1-pisteet 0,9 ) — osoitti merkitsevien reseptiominaisuuksien olemassaolon. Ne vaikuttivat merkittävästi reseptien suosioon. Käyttämiimme tietojoukkoihin vaikuttivat merkittävästi käyttäjien sitoutumisominaisuudet, erityisesti vastaanotettujen arvioiden ja kommenttien määrä. Toisin sanoen reseptit, jotka saivat enemmän huomiota kommenteissa ja arvioissa, olivat yleensä suositumpia. Lisäksi selvisi, että huomattava osa Valion resepteistä kuuluu keskitason terveyspisteiden alueelle (arvioituna FSA Scorella), ja mielenkiintoisesti, vähemmän terveellisiksi katsotut reseptit saavat käyttäjiltä yleensä korkeamman keskiarvon. Tämä tutkimus edistää ymmärrystämme verkkoreseptien suosioon vaikuttavista tekijöistä ja tarjoaa arvokasta näkemystä nykypäivän ruoanlaittotottumuksista Suomessa

Understanding aesthetic aspects of cross-cultural food preferences with online recipe platforms

Food recommender systems offer the potential to provide meal suggestions that account not only for user preferences, but also reflect ideals, such as healthfulness and sustainability. Predicting user food preferences is, however, a challenging problem given the diverse factors that influence what an individual likes to eat. Culture is known to be one of the factors. Food across cultures shows differences in many aspects, for example, in aesthetics. This doctoral work investigates the aesthetic aspects, particularly in terms of visual appearance and flavour, of food. Focusing on how food looks and tastes in different cultures and how human food choices are related to these, this work aims at analysing and interpreting human food aesthetic preferences across cultures. Based on these observations, I show implications for the development of food recommender systems. Online recipe platforms from three distinct cultures, including China, US and German, serve as data resources for this research, as there are a large amount of data representing the corresponding food cultures (e.g., online recipes) and user food preferences (e.g., ratings, bookmarks). Computer science techniques are employed to extract the aesthetic information, including visual features from the online recipe images and flavour compounds of the ingredients, allowing for successively classification and prediction tasks to be performed on these data by means of machine learning approaches. The algorithmic results show that online recipes differ in visual appearance and flavour across recipe portals. This, together with the findings from the follow-up user study demonstrating how culture biases human interpretation of online recipe images, highlights the impact of culture in human food choices. The algorithms also confirm that human online food preferences are aesthetically driven within each culture. Furthermore, stable patterns in aesthetic food preferences across cultures can be identified, which is supported and justified by means of further user study and exploratory analyses. The research presented in this doctoral work increases the understanding of human cross-cultural food preferences. Moreover, findings from this thesis emphasise the merit of considering the synthetic impact of culture and aesthetics into food recommendation and provide a promising perspective for the development of food recommender systems by incorporating stable patterns in cross-cultural aesthetic food preferences

Knowledge Graphs Evolution and Preservation -- A Technical Report from ISWS 2019

One of the grand challenges discussed during the Dagstuhl Seminar "Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web" and described in its report is that of a: "Public FAIR Knowledge Graph of Everything: We increasingly see the creation of knowledge graphs that capture information about the entirety of a class of entities. [...] This grand challenge extends this further by asking if we can create a knowledge graph of "everything" ranging from common sense concepts to location based entities. This knowledge graph should be "open to the public" in a FAIR manner democratizing this mass amount of knowledge." Although linked open data (LOD) is one knowledge graph, it is the closest realisation (and probably the only one) to a public FAIR Knowledge Graph (KG) of everything. Surely, LOD provides a unique testbed for experimenting and evaluating research hypotheses on open and FAIR KG. One of the most neglected FAIR issues about KGs is their ongoing evolution and long term preservation. We want to investigate this problem, that is to understand what preserving and supporting the evolution of KGs means and how these problems can be addressed. Clearly, the problem can be approached from different perspectives and may require the development of different approaches, including new theories, ontologies, metrics, strategies, procedures, etc. This document reports a collaborative effort performed by 9 teams of students, each guided by a senior researcher as their mentor, attending the International Semantic Web Research School (ISWS 2019). Each team provides a different perspective to the problem of knowledge graph evolution substantiated by a set of research questions as the main subject of their investigation. In addition, they provide their working definition for KG preservation and evolution

Effects and challenges of using a nutrition assistance system: results of a long-term mixed-method study

Healthy nutrition contributes to preventing non-communicable and diet-related diseases. Recommender systems, as an integral part of mHealth technologies, address this task by supporting users with healthy food recommendations. However, knowledge about the effects of the long-term provision of health-aware recommendations in real-life situations is limited. This study investigates the impact of a mobile, personalized recommender system named Nutrilize. Our system offers automated personalized visual feedback and recommendations based on individual dietary behaviour, phenotype, and preferences. By using quantitative and qualitative measures of 34 participants during a study of 2–3 months, we provide a deeper understanding of how our nutrition application affects the users’ physique, nutrition behaviour, system interactions and system perception. Our results show that Nutrilize positively affects nutritional behaviour (conditional R2=. 342) measured by the optimal intake of each nutrient. The analysis of different application features shows that reflective visual feedback has a more substantial impact on healthy behaviour than the recommender (conditional R2=. 354). We further identify system limitations influencing this result, such as a lack of diversity, mistrust in healthiness and personalization, real-life contexts, and personal user characteristics with a qualitative analysis of semi-structured in-depth interviews. Finally, we discuss general knowledge acquired on the design of personalized mobile nutrition recommendations by identifying important factors, such as the users’ acceptance of the recommender’s taste, health, and personalization