Recipe Recommendation Research Articles

Exploring Chinese Dietary Habits Using Recipes Extracted From Websites

As more and more cooking recipes are published on the websites by people from different regions, it becomes available to explore the dietary habit of different regions by analyzing their recipes. As a big country owning 32 provinces and autonomous regions, China has generated a series of different cuisines. In this paper, we explore the dietary habits of Chinese by analyzing recipes of different Chinese cuisines. We collected more than 14000 cooking recipes for the 20 different Chinese cuisines from the food-focused website. Our investigation is divided into six perspectives, including the diversity of ingredients, the manifest ingredients, the complexity of cooking, the similarity of dishes, and the dietetic therapy. By the study of recipes with Chinese cuisines, we find strong geographical effects of recipes from different Chinese cuisines. In order to help people understand Chinese habitual collocation of ingredients, we use the Apriori algorithm to find frequent itemsets. Finally, we propose a content-based online recipe recommendation engine to bring some suggestions for people. We believe that our study is helpful to get a deep insight into Chinese dietary culture.

A Recipe Decision Support System Using Knowledge Information and Agent

We report on the development of a recipe recommendation system using collaborative filtering and impression words. As a user interface (UI), we adopted MikuMikuDanceAgent (MMDAgent). In our system, the first recommendation process using collaborative filtering is terminated based on the previously decided condition, after which the second recommendation process identifies the recipe that is most similar to past successful recommendations from recipes that have not been recommended thus far. As a final step, one recipe is selected from all successfully recommended recipes.

Recipe Recommendation

Recipe Recommendation

Culinary Recipe Recommendation based on Text Analytics

Many researchers and practitioners have studied the recipe recommendation, and that problem is not only to find the tasty dishes based on the individual’s preference, but also to generate new ones. In the digital age, understanding and utilizing text data is one of the most important part in the knowledge discovery. In this paper, we proposed how to use text analysis in the recipe recommendation problem and provided the insights to design new recipes.

Dynamic feature weighting based on user preference sensitivity for recommender systems

With the development of information technology, the issue of information overload has become an ever-increasing problem throughout this decade. In order to provide useful and interesting information to users, recommender systems were proposed to actively select preferable items for users who potentially desired. In this paper, we focus on personalizing aspects of recommender systems, since it is observed that the weights of the dimensions should be distinct for every user when measuring the similarities between items. To achieve this outcome, we suggest that the dimensions of items should be classified into different types such as numeric and textual, and then the average preferences (AP) should be calculated for these dimensions based on all the users in the recommender system. Then, the preference sensitivities (PS) for every dimension can be calculated for every user so that finally the dynamic feature weights can be applied in the calculations. All these aforementioned methodologies are evaluated via experiments using a Recipe Recommender System which achieves an 81.0% overall satisfaction rate on a basis of more than 50 real volunteer users from diverse educational background.

A Recipe Recommendation System Based on Regional Flavor Similarity

One of the factors that affect food choice is flavor preferences. Although the flavors of cuisines vary from one region to another, there are flavor similarities among the cuisines from geographically adjacent regions. This paper presents a recipe recommendation system to recommend a set of dishes from the various Chinese regional cuisines for a certain flavor preference in terms of flavor similarity. The flavor similarities among the cuisines are determined using our previously developed algorithms. First, the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm is used to calculate the ingredient preferences of the regional cuisines, on the basis of which, each dish of the regional cuisines is given a score. Then, we use the cosine similarity to measure the flavor similarities among the regional cuisines. Finally, the Tidal-Trust algorithm is employed to choose the dishes with the most similar flavors and recommend them to the user. The results of the questionnaire evaluation for the system show the recommendations from the system are reasonable and acceptable from professional chefs’ point of view.

A Recipe Recommendation System Based on Regional Flavor Similarity

A Recipe Recommendation System Based on Regional Flavor Similarity

Quantifying textual terms of items for similarity measurement

It is well known that recommender systems rely on the similarity between items to be recommended. Most current research projects in this area utilize traditional similarity measurement algorithms, such as cosine distance or derivatives of these. However, the most challenging problem facing these approaches is to quantify the non-numerical attributes of items. This is quite intractable and cannot be solved with regular similarity measurement algorithms. This paper proposes two novel methods, the Taxonomic Trees Similarity Measurement (TTSM) and the Decomposed Structures Similarity Measurement (SDSM), so that the similarities between the textual attributes can be measured using numeric values after they have been quantified. Also, the quantifying process is completely based on the semantic meanings of the textual terms. Furthermore, a maximized term matching (MTM) mechanism is induced and applied to the group-based textual attributes of items in recommender systems. Finally, we evaluate our methods by implementing a recipe recommender system which achieves a 74.4% overall satisfaction rate as evaluated by real users.

RECIPE RETRIEVAL USING INGREDIENT RECOGNITION

Based on the generic object recognition, we propose a cooking recipe recommendation system on an android smart phone which includes food (vegetable and meats) detection involving object recognition. The system which we have proposed gives the real time recognition of the food stuff and using with a recommendation system, helps retrieve recipes including those food ingredients. When the user points a smart phone camera towards the food items, the recipes are retrieved instantly based on certain ranking.

Cuisine Prediction based on Ingredients using Tree Boosting Algorithms

Objective: This paper aims at predicting the cuisine based on the ingredients using tree boosting algorithm. Methods/ Analysis: Text mining is important tool for data mining in Ecommerce websites. Ecommerce business is growing with significant rate both in Business-to-Business (B2B) and Business to Customer (B2C) categories. The machine learning based models and prediction method are used in real world ecommerce data to increase the revenue and study customer behavior. Many online cooking and recipe sharing websites have ardent to evolution of recipe recommendation system. In this paper, we describe a scalable end to end tree boosting system algorithms to predict cuisine based on the ingredients and also explored different data analysis and explained about the dataset types and their performances. Novelty/ Improvement: An accuracy of about 80% is obtained for cuisine prediction using XG-Boosting algorithm.

Bargaining agents based system for automatic classification of potential allergens in recipes

The automatic recipe recommendation which take into account the dietary restrictions of users (such as allergies or intolerances) is a complex and open problem. Some of the limitations of the problem is the lack of food databases correctly labeled with its potential allergens and non-unification of this information by companies in the food sector. In the absence of an appropriate solution, people affected by food restrictions cannot use recommender systems, because this recommend them inappropriate recipes. In order to resolve this situation, in this article we propose a solution based on a collaborative multi-agent system, using negotiation and machine learning techniques, is able to detect and label potential allergens in recipes. The proposed system is being employed in receteame.com, a recipe recommendation system which includes persuasive technologies, which are interactive technologies aimed at changing users’ attitudes or behaviors through persuasion and social influence, and social information to improve the recommendations.

A Linear General Type-2 Fuzzy-Logic-Based Computing With Words Approach for Realizing an Ambient Intelligent Platform for Cooking Recipe Recommendation

This paper addresses the need to enhance transparency in ambient intelligent environments by developing more natural ways of interaction, which allow the users to communicate easily with the hidden networked devices rather than embedding obtrusive tablets and computing equipment throughout their surroundings. Ambient intelligence vision aims to realize digital environments that adapt to users in a responsive, transparent, and context-aware manner in order to enhance users’ comfort. It is, therefore, appropriate to employ the paradigm of “ computing with words ” (CWWs), which aims to mimic the ability of humans to communicate transparently and manipulate perceptions via words. One of the daily activities that would increase the comfort levels of the users (especially people with disabilities) is cooking and performing tasks in the kitchen. Existing approaches on food preparation, cooking, and recipe recommendation stress on healthy eating and balanced meal choices while providing limited personalization features through the use of intrusive user interfaces. Herein, we present an application, which transparently interacts with users based on a novel CWWs approach in order to predict the recipe's difficulty level and to recommend an appropriate recipe depending on the user's mood, appetite, and spare time. The proposed CWWs framework is based on linear general type-2 (LGT2) fuzzy sets, which linearly quantify the linguistic modifiers in the third dimension in order to better represent the user perceptions while avoiding the drawbacks of type-1 and interval type-2 fuzzy sets. The LGT2-based CWWs framework can learn from user experiences and adapt to them in order to establish more natural human–machine interaction. We have carried numerous real-world experiments with various users in the University of Essex intelligent flat. The comparison analysis between interval type-2 fuzzy sets and LGT2 fuzzy sets demonstrates up to 55.43% improvement when general type-2 fuzzy sets are used than when interval type-2 fuzzy sets are used instead. The quantitative and qualitative analysis both show the success of the system in providing a natural interaction with the users for recommending food recipes where the quantitative analysis shows the high statistical correlation between the system output and the users’ feedback; the qualitative analysis presents social science evaluation confirming the strong user acceptance of the system.

Expert E-Prescription Application (EEPA) using Forward Chaining Method

Medical errors in reading doctor’s handwritten prescription are common in hospitals. These problems may harm the patient’s conditionif the pharmacist misreads a recipe given by the doctor. Therefore, Electronic prescribing (e-prescription) system is urgently needed to overcome faults in recipe prescription process. Pharmacist or medical staff can easily read a handwritten recipe by using an eprescription application. Here, we develop Expert E-Prescription Application (EEPA) utilized with recipe recommendation feature based on the disease symptoms. In this application, there are three types of user with the different access rights for each of them; doctor, pharmacist, and receptionist. EEPA facilitate the receptionist in registering, filling, and updating patient data. Doctor can generate a recommended recipe by typing the symptoms and also view the history of patient medical records. Pharmacist can also check patient’s prescription and give a medicine based on patient id number. Test result of this application which justified by the expert (doctor) give the accuracy of 91,67%.

A Cooking Recipe Recommendation System with Visual Recognition of Food Ingredients

In this paper, we propose a cooking recipe recommendation system which runs on a consumer smartphone as an interactive mobile application. The proposed system employs real-time visual object recognition of food ingredients, and recommends cooking recipes related to the recognized food ingredients. Because of visual recognition, by only pointing a built-in camera on a smartphone to food ingredients, a user can get to know a related cooking recipes instantly. The objective of the proposed system is to assist people who cook to decide a cooking recipe at grocery stores or at a kitchen. In the current implementation, the system can recognize 30 kinds of food ingredient in 0.15 seconds, and it has achieved the 83.93% recognition rate within the top six candidates. By the user study, we confirmed the effectiveness of the proposed system.

Cooking Recipe Recommendation System Based on CBR

Cooking Recipe Recommendation System Based on CBR

A Next-Generation Recipe Recommendation System for Health and Environment

 Abstract—In recent years, recipe search sites have proliferated with the spread of the Internet and the smartphone. Many recipes are available on the Web. In addition, today's Japan is experiencing problems such as aging and the increase in persons suffering from lifestyle-related disease due to westernization of their eating habits. It is often necessary for them to manage their eating habits. For home-bound aging and living alone, having to think about a menu every day is a major burden. At the same time, as the carbon dioxide emission of the whole world increases year by year, significant action on carbon dioxide reduction is underway in Japan. For example, there is a Carbon Foot Print (CFP) on which the Ministry of Agriculture, Forestry and Fisheries works. This study seeks to support menu management and the construction of a system which is conscious of the carbon dioxide emission associated with a given meal. It uses recipes on the Web and prepares a recommended menu. We propose a system that displays the carbon dioxide emission of each menu. Finally, we had subjects use this system and determined the effectiveness of this study by performing a user evaluation.

Prediction of interface preferences with a classifier selection approach

Interaction in smart environments should be adapted to the users’ preferences, e.g., utilising modalities appropriate for the situation. While manual customisation of a single application could be feasible, this approach would require too much user effort in the future, when a user interacts with numerous applications with different interfaces, such as e.g. a smart car, a smart fridge, a smart shopping assistant etc. Supporting user groups, jointly interacting with the same application, poses additional challenges: humans tend to respect the preferences of their friends and family members, and thus the preferred interface settings may depend on all group members. This work proposes to decrease the manual customisation effort by addressing the cold-start adaptation problem, i.e., predicting interface preferences of individuals and groups for new (unseen) combinations of applications, tasks and devices, based on knowledge regarding preferences of other users. For predictions we suggest several reasoning strategies and employ a classifier selection approach for automatically choosing the most appropriate strategy for each interface feature in each new situation. The proposed approach is suitable for cases where long interaction histories are not yet available, and it is not restricted to similar interfaces and application domains, as we demonstrate by experiments on predicting preferences of individuals and groups for three different application prototypes: recipe recommender, cooking assistant and car servicing assistant. The results show that the proposed method handles the cold-start problem in various types of unseen situations fairly well: it achieved an average prediction accuracy of $$72 \pm 1\,\%$$ . Further studies on user acceptance of predictions with two different user communities have shown that this is a desirable feature for applications in smart environments, even when predictions are not so accurate and when users do not perceive manual customisation as very time-consuming.

Semantic Reasoning-Based Chinese Recipe Recommender System

For the blank of the recommender system for chinese recipes, this paper uses OWLS-WSDL to build a semantic reasoning-based chinese recipe recommender system. This system through the tool of Protégé to establish the ontology of chinese recipes and then add rules for ontology reasoning. On this basis bring out a catering algorithm, using Euclidean distance and Jaccard to calculate the similarity between the dishes. According to the similarity as well as user preference, provides a quick means of siding dishes for users.

Distributed Cooking Recipe Recommendation and Adaptation

In this paper, we propose a distributed recipe recommendation mechanism that utilizes social information for adaptive recipe recommendation in a peer-to-peer (P2P) network. A recipe flavor model is first proposed for modeling recipes and validating recipe adaptations. Peers in the network group themselves into communities in which members share common preferences of recipe data. This is helpful to visit more relevant peers when the query scope is fixed thus improve the performance of recommendation. Based on a graph-based recipe representation, we propose a recipe similarity measure and a filtering algorithm to generate candidates of cooking recipes to be recommended. A recipe adaptation method is also proposed in order to better match users’ preferences. Experiments are conducted for the evaluation of the proposed model and a prototype system for cooking recipe recommendation is also presented.

Recipe Recommendation for a Diet Considering User's Various Conditions

Recipe Recommendation for a Diet Considering User's Various Conditions