Food Recommendation System Research Articles

Towards a new healthy food decision-making system

Latterly, food recommendation systems have received high attention due to their importance to healthy living. On the recommendation domain, most studies focus on recommendations that suggest healthy products for each user based on their profiles. These types of recommender systems offer additional functionality to persuade users to change their buying behavior profitably. However, these systems must highlight the health preferences of the users and their health problems must be adequately taken into account. In this work, healthy food products recommender systems (RS) are our interest study and more specifically using content-based filtering. We represented this content by the food product composition. Our goal was to provide a healthy recommendation to consumers or citizens around the world, especially at this time when disease abounds. Thus, we developed our new healthy recommendation system (HRS). In this paper, we present a new recommendation process for individuals in the area of healthy eating. Furthermore, we analyze the existing state of the art in recommender system techniques and implement an algorithm that responds to this new process with very satisfactory results from the beginning, to conclude we discuss the research challenges related to the development of this kind of HRS.

Dietary Behavior Based Food Recommender System Using Deep Learning and Clustering Techniques

Deep learning algorithms have been highly successful in various domains, including the development of collaborative filtering recommender systems. However, one of the challenges associated with deep learning-based collaborative filtering methods is that they require the involvement of all users to construct the latent representation of the input data, which is then utilized to predict the missing ratings of each user. This can be problematic as some users may have different preferences or interests, which may affect the accuracy of the prediction generation process. The research proposed a food recommender system, which tries to find users with similar dietary behavior and involve them in the recommendations generation process by combining clustering technique with denoising autoencoder to generate a rate prediction model. It is applied to “Food.com Recipes and Interactions” dataset. RMSE score was used to evaluate the performance of the proposed model which is 0.1927. It outperformed the other models that used autoencoder and denoising autoencoder without clustering where the RMSE values are 0. 4358 and 0.4354 consequently.

A healthy nutrition suggestion model for indian women sports players & active youth using long short‐term memory

AbstractSports nutrition is the balanced diet or diet chart that helps to improve performance of sports persons. It is globally accepted that Indian foods are rich in nutrition. It is greatly preferred by yogis, gurus and dieticians to intake Indian foods in order to maintain a wellness and healthy lifestyle. Smart watches, wearable devices, mobile applications and digital portals are available to suggest foods to the sports persons. But software application based on Indian foods specifically for women athletes are not exists so far. In this paper an intelligent food recommendation system based on Long Short‐Term Memory (LSTM) and LSTM with GRU is proposed to suggest meals for women athletes. LSTM has connections and it processes the entire sequence of data thus, it is week suitable for suggestion models. The performance of the model is validated using evaluation metrices and the result demonstrate its effectiveness.

Understanding and predicting cross-cultural food preferences with online recipe images

Predicting food preferences is challenging due to the numerous factors that can influence individual taste. Cultural influences are one such factor that can significantly impact food preferences. Irrespective of culture, however, food visual aesthetics drive food choice. With this in mind, we study 15,000 images of food from prominent recipe portals in China, the US and Germany with the aim of identifying common visual trends related to food choice. We report that distinguishing between appreciated and less appreciated recipes based on visual features through multiple machine learning experiments is possible, with a maximum accuracy of 67%. The classifiers trained on one culture, when applied to other cultures, reveal that appreciated recipes from US and German portals share visual similarities, while the visual aspects making recipes attractive for Chinese users differ. Complementing our machine learning experiments, we conducted a user study with 450 participants from the three cultures. Participants are asked to rank recipes based on their appeal and provide justification through answering 19 questions. The results reveal significant predictors of preference, including perceived appearance and taste across all countries, and negative perception of perceived healthiness for US and German participants. Analysis of 77 consistently labelled appealing/non-appealing images reveal significant variations in low-level visual features such as colourfulness, sharpness, and contrast. These findings suggest that these low-level visual features may play a critical role in determining whether an image is perceived as appealing or not across different cultures. Our results offer promising insights into the development of cross-culture food recommender systems. By demonstrating common ground across food cultures, we believe that these systems can provide personalised meal suggestions that reflect user’s preferences while incorporating ideals such as healthfulness and sustainability.

Recipe Networks and the Principles of Healthy Food on the Web

People increasingly use the Internet to make food-related choices, prompting research on food recommendation systems. Recently, works that incorporate nutritional constraints into the recommendation process have been proposed to promote healthier recipes. Ingredient substitution is also used, particularly by people motivated to reduce the intake of a specific nutrient or in order to avoid a particular category of ingredients due for instance to allergies. This study takes a complementary approach towards empowering people to make healthier food choices by simplifying the process of identifying plausible recipe substitutions. To achieve this goal, this work constructs a large-scale network of similar recipes, and analyzes this network to reveal interesting properties that have important implications to the development of food recommendation systems.

Food Recommendation System using One-Stage Algorithm

Abstract: There are a number of reasons why it's helpful when people suggest their favourite foods. There has been a rise in the use of automated systems that provide recommendations for various forms of food, including recipes, restaurants, and grocery stores. Scientists have been looking at these sorts of systems for a long time, and their findings show they may help individuals not just identify food they would want to eat, but also fuel themselves more properly. This article summarises the current status of so-called food recommender systems, focusing on both foundational and cutting-edge approaches to the issue, as well as essential specialisations such food recommendation systems for groups of users or systems that encourage healthy eating. Assisting consumers in finding their ideal meal is a crucial function of food recommender systems. Choosing what to eat is a complicated and multi-faceted process that is impacted by many things, including the ingredients, the look of the dish, the user's own preference on food, and many situations, such as what had been eaten at the previous meal. This paper formalises the meal suggestion issue as forecasting user preference on recipes using three essential factors: the user's (and other users') history, the recipe's components, and the recipe's descriptive picture. In order to solve this difficult issue, we design a special neural network. The recommender creates a model of eating habits based on responses to previous surveys. The model is then used to prompt respondents, based on the meals they have previously picked, to submit any linked items they may have forgotten to mention. Model-generated cues were evaluated against those that were manually programmed by dietitians.

Food Recommendation System

In the past, people obtained suggestions for restaurants from friends or other conventional sources or sites. Although this method is straight forward and user friendly, it has some severe limitations. First, the recommendations from friends or other common people are limited to those places they have visited before. Thus, the user is not able to gain information about places less visited by their friends. Besides that, there is a chance of users not liking the place recommended by their friends. Second, the information provided by the site can often be biased; thus, the information provided cannot always consider being accurate. Recommender systems are widely deployed to predict the preferences of users to items. They are popular in helping users find restaurants and products in general. The primary aim of the Food Recommendation System is to suggest the best restaurant nearby along with food using the given user preferences. This application takes the food preferences and ratings into consideration to recommend the food to the users.

MWSMO: Multi-objective Whale Slime Mold Optimization based Food Recommendation system for Diabetes patient using GAN model

MWSMO: Multi-objective Whale Slime Mold Optimization based Food Recommendation system for Diabetes patient using GAN model

Health-Aware Food Recommendation Based on Knowledge Graph and Multi-Task Learning.

Current food recommender systems tend to prioritize either the user's dietary preferences or the healthiness of the food, without considering the importance of personalized health requirements. To address this issue, we propose a novel approach to healthy food recommendations that takes into account the user's personalized health requirements, in addition to their dietary preferences. Our work comprises three perspectives. Firstly, we propose a collaborative recipe knowledge graph (CRKG) with millions of triplets, containing user-recipe interactions, recipe-ingredient associations, and other food-related information. Secondly, we define a score-based method for evaluating the healthiness match between recipes and user preferences. Based on these two prior perspectives, we develop a novel health-aware food recommendation model (FKGM) using knowledge graph embedding and multi-task learning. FKGM employs a knowledge-aware attention graph convolutional neural network to capture the semantic associations between users and recipes on the collaborative knowledge graph and learns the user's requirements in both preference and health by fusing the losses of these two learning tasks. We conducted experiments to demonstrate that FKGM outperformed four competing baseline models in integrating users' dietary preferences and personalized health requirements in food recommendations and performed best on the health task.

Food choice and the epistemic value of the consumption of recommender systems: the case of Yuka’s perceived value in France

ABSTRACT Food Recommender Systems (RecSys) are innovative knowledge systems that inform consumers of food choices according to criteria, including nutritional content, health concerns, production method, carbon footprint or other social and ethical considerations. They raise important questions at the intersection of technology accuracy and today evolving consumers’ knowledge seeking behaviours, which implies to unpack the epistemic value of food RecSys. This study investigates the drivers of the perceived value of food RSs consumption by proposing a model that establishes via PLS-SEM (n = 253) a positive relationship between the Yuka company’s food RecSys’ epistemic value and its perceived value. The model demonstrates that Yuka RecSys’ epistemic value relies on the disciplinary drivers of compatibility, self-confidence, and consumer innovativeness, and the problematising drivers of memory and learning, which come from using the application. The perceived value of food RecSys is found to relate to RecSys epistemic value beyond the functional accuracy aspects of recommendation algorithms. Results highlight the importance of developing a refined understanding of epistemic value considering the consumption of RecSys. RecSys’ developers, retailers, food manufacturers and policy makers must work on better mapping and adjusting information through consumers socialised RecSys’ usage to shape the design of the next generation RecSys.

FoodRecNet: a comprehensively personalized food recommender system using deep neural networks

FoodRecNet: a comprehensively personalized food recommender system using deep neural networks

Healthy Food Recommender System for Obesity Using Ontology and Semantic Web Rule Language

Today's lifestyle and eating patterns tend to be irregular due to busyness. People prefer eating foods that are fast and easy to obtain, but often lack knowledge of the nutritional content in them. These eating patterns lead to unbalanced nutrition and can cause various health problems and diseases, such as overweight and obesity. Due to a lack of information, people often turn to drugs instead of learning about healthy diets, making it difficult for them to determine what menu to choose or what type of food to consume. While there have been many studies to recommend healthy food based on user preferences, there is currently no recommender system that includes serving size and budget for each daily food recommendation that is implemented in a chatbot framework. This study proposes using ontology and the Semantic Web Rule Language (SWRL) to store knowledge in the ontology and then process it using SWRL to produce food recommendations based on user preferences. From a sample of user data which obtained 170 recommended meal menus. System performance is pretty good. Based on the validation results from nutritionists, the precision value was 0.852941, the recall was 1, and F-score of 0.920634 So that a healthy food recommendation system can be used to help the user follows a diet that meets his nutritional needs and is within his budget needed

A unified approach to designing sequence-based personalized food recommendation systems: tackling dynamic user behaviors

The recommender system (RS) is a well-known practical application of the state-of-the-art information filtering and machine learning technologies. Traditional recommendation approaches, including collaborative and content-based filtering techniques, have been widely employed to provide suggestions in RSs, where the user-item interaction matrix is the primary data source. In many application domains, interactions between users and items are more likely to be dynamic rather than static, and thus dynamic user behaviors should be taken into account when solving recommendation tasks in order to provide more accurate suggestions. In this work, we consider the sequentially ordered information from user-item interactions in the RSs where a sequence-based recommendation model is put forward with applications to the food recommendation scenario. Furthermore, the long short-term memory (LSTM) network is employed as the building block to establish such a recommendation model, and a collaborative filtering unit is adopted to make personalized food recommendation. The proposed LSTM-based RS is successfully applied to a real-world food recommendation data set. Experimental results demonstrate that the developed method outperforms some currently popular RSs in terms of precision, recall, mean average precision and mean reciprocal rank in food recommendation.

Design and Implementation of a Recommendation System for Buying Fresh Foods Online Based on Web Crawling

As shopping patterns have gradually shifted from offline to online mode, and with recent lockdowns during the coronavirus disease 2019 (COVID-19) pandemic restricting foreign trade and accelerating the growth of the domestic economy, digital transformation has become a major strategy for many retailers to support and expand their businesses. With the pandemic becoming a turning point, the business of major e-commerce companies in Taiwan in the retail of dry goods has grown significantly, and it has driven the online sales of fresh products as well. In this era of fierce competition, it is especially important to find a way that enables consumers to quickly find ideal fresh products on multiple platforms, shortens the time for price comparison, and improves the efficiency of online shopping. This study uses the Python programming language to write a web crawler program that captures product information from fresh food e-commerce platforms, including product introduction, price, origin, and sales volume, and then defines the relevant status of the product, such as product popularity. Accordingly, through Chinese text segmentation and term-frequency calculation, it aims to classify the product names and introductions into frequently occurring words and use them as product-related labels. Finally, the program combines the product information processing results and product-related labels to construct an online fresh food recommendation system. The results of the proposed system show that it reduces the time and energy spent comparing prices. It can also guide consumers to browse products that may be of interest using relevant tags and increase consumption efficiency by helping them find the ideal item when shopping.

A novel healthy and time-aware food recommender system using attributed community detection

Food recommendation systems aim to provide recommendations according to a user’s diet, recipes, and preferences. These systems are deemed useful for assisting users in changing their eating habits towards a healthy diet that aligns with their preferences. Most previous food recommendation systems do not consider the health and nutrition of foods, which restricts their ability to generate healthy recommendations. This paper develops a novel health-aware food recommendation system that explicitly accounts for food ingredients, food categories, and the factor of time, predicting the user’s preference through time-aware collaborative filtering and a food ingredient content-based model. Based on the user's predicted preferences and the health factor of each food, our model provides final recommendations to the target user. The performance of our model was compared to several state-of-the-art recommender systems in terms of five distinct metrics: Precision, Recall, F1, AUC, and NDCG. Experimental analysis of datasets extracted from the websites Allrecipes.com and Food.com demonstrated that our proposed food recommender system performs well compared to previous food recommendation models.

Novel Nutritional Recipe Recommendation

Food is essential for living and is the foremost important energy source, making us do all the work. Nowadays, the variability in these food items is increasing. To find out about any new dish or recipe, we mainly depend upon people around us or by trial-and-error method, but neither method tells us about its nutritional content. Since the web has begun to grow, the advent of food recommender systems has made food suggestions easier but these systems work only on the feedback provided by the customer. Hence, here comes a requirement for a nutritional-based recommender system that considers ratings and nutrition and provides the user with an absolute best recommendation so that the users’ taste preferences and well-being are given equal priority. This study intends to use graph embedding approaches to develop a food recipe recommender system, which uses the ingredients’ nutritional value alongside the recipe’s taste and customer feedback. These food recommender systems can impact people’s dietary practices, as their suggestions are both healthy and relevant. People can now eat healthily without being compromised on taste.

Healthy Food Recommendation Using a Time-Aware Community Detection Approach and Reliability Measurement

Food recommendation systems have been increasingly developed in online food services to make recommendations to users according to their previous diets. Although unhealthy diets may cause challenging diseases such as diabetes, cancer, and premature heart diseases, most of the developed food recommendation systems neglect considering health factors in their recommendation process. This emphasizes the importance of the reliability of the recommendation from the health content perspective. This paper proposes a new food recommendation system based on health-aware reliability measurement. In particular, we develop a time-aware community detection approach that groups users into disjoint sets and utilizes the identified communities as the nearest neighbors set in rating prediction. Then, a novel reliability measurement is introduced by considering both the health and accuracy criteria of predictions to evaluate the reliability of predicted ratings. Also, the unreliable predictions are recalculated by removing ineffective users from the nearest neighbors set. Finally, the recalculated predictions are utilized to generate a list of foods as recommendations. Different experiments on a crawled dataset demonstrate that the proposed method enhances the performance around 7.63%, 6.97%, 7.37%, 15.09%, and 16.17% based on precision, recall, F1, normalized discounted cumulative gain (NDCG), and health metrics, respectively, compared to the second-best model.

Consumption Variety in Food Recommendation

This research explores the justification and implications of incorporating consumption variety into mobile-based food recommendation systems. Our study makes use of data from a popular mobile fitness app, in which we can observe large volumes of daily food logs of thousands of users. We first confirm that consumption variety is associated with lower overall calories consumed, higher vegetable consumption, and lower snack consumption. Motivated by these empirical patterns about variety and eating, we then seek out to design a novel multicriteria food recommendation system (FOODVAR) that can accommodate for variety in recommended foods. We then assess the impact of including this additional variety criterion in recommendation system performance, where we show that the incorporation of variety improves the algorithm’s evaluation metrics.

A Survey on IOMT based Food Recommendation System

A Survey on IOMT based Food Recommendation System

Android Food Diet Plan for Curing Diseases

Abstract: In the healthcare industry, the patient's nutrition is a very important key factor in their treatment process. Every user has their own specific nutritional needs and requirements. An appropriate nutrition policy can help the patient's recovery process and possible symptoms. Food recommender systems are platforms that offer personalized suggestions of food diet plans to users. Moreover, there is a lack of usage of food recommendation systems in the healthcare sector. Multiple challenges in representing the domain of food and the patient's needs to make the user’s life easy in using this android application. The present project aims to develop a platform for the users to provide meals, based on their health conditions.