IJARCCE ISSN (Online) 2278-1021 ISSN (Print) 2319-5940 International Journal of Advanced Research in Computer and Communication Engineering Vol. 8, Issue 3, March 2019 Copyright to IJARCCE DOI 10.17148/IJARCCE.2019.8313 73 Recipe Recommendation Based on Ingredients using Machine Learning Ms. Soundarya Desai 1 , Ms. Pooja Patil 2 , Mr. Pratik Shinde 3 , Mr. Azhar Sayyed 4 , Prof. Rohini Bhosale 5 Student, Comp Dept, KJEI‟s Trinity College of Engineering and Research Pisoli, Pune, India 1,2,3,4 Guide, Comp Dept, KJEI‟s Trinity College of Engineering and Research Pisoli, Pune, India 5 Abstract: Thinking of what to cook is also a difficult problem. To attract children liking, parent need to exchange the menu every day. Parents not only think to what recipe to changes, they also need to consider the nutrition that their children taken. Besides that, some people will forget buy ingredients to stock in their kitchen. This will become a problem when they want to prepare meal within short time. It is difficult to think what to cook with limited ingredient that in the kitchen Many people often cook a dish with a cooking recipe on Websites and magazines. The listed ingredients in the recipe sometimes cannot be prepared. This paper propose a recommendation system for different ingredients. The recommendation ingredients based on co-occurrence frequency of ingredients on recipe database and ingredient category stored in a cooking ontology. For object detection open CV is used which is java based. Basically we are using this platform for feature extraction of ingredients. Grey scale of the image is calculated, further histogram is generated which helps in identification of the food items. Keywords: Recipe, Ingredient, User, Interest I. INTRODUCTION It is natural to think that couples who work at a company or a person who lives by her/himself want to cook food for themselves as quickly and easily as possible when they are busy. However, to keep having the same food they can easily cook fed them up, therefore, it should be preferable for them to be recommended a variety of food that they can cook "easily". Currently, there are so many Web sites for cooking recipes, and there are also recipes regarded as "easy" to cook. However, those recipes are not estimated as "easy" by taking user's conditions into account. Therefore, in this system, we aim to develop a method to recommend "easy" cooking recipes by analyzing the content of recipes and considering user's conditions and then develop a recommendation system with the proposed method. Recommender systems are very helpful when the recommended item is something that the user has not seen in the past any. For example, popular movies of a preferred genre would not mostly to be novel to the user. Repeated recommendation of popular items can also lead to decrease in sales diversity. II. LITERATURE SURVEY A Recipe Recommendation System Based on Regional Flavor Similarity Lin-rong GUO, Shi-zhong YUAN* , Xue-hui MAO and Yi-ning GU School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China.[1] 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. “Constraint based recipe recommendation using forward checking algorithm “, Kirti R. Pawar ; Tushar Ghorpade ; Rajashree Shedge 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI)[2]