Building a Healthy Food Recommendation System

As industries in various fields are expanding, people are often confused to choose the best from different choices and decide what would be best for them. Recommendation systems are a type of information filtering that studies the human interests and presents lists of items which are likely of user preference and help people make decisions in these situations. Here we build a food recommendation system which give recommendations on the basis of considering the users preferences as well as their nutritional needs.

Abstract

For building of the recommendation system, machine learning algorithms based on models such as Collaborative Filtering, Content-based filtering, and a Hybrid approach is used. The built – system would take input as user’s previous history and a set of health-related questionnaire, and provides a list of food items which satisfies user expectations and which can be considered for further recommendation.

At present we have many systems (apps) that recommend us eateries based on our location and also facile ordering.

But we hardly find apps that recommends food based on our tastes and other preferences. Further the unhealthy food choices and improper food diets has become the primary cause of various illness and obesity. Therefore, a system that takes the interests of the customer, providing them a choice of setting their likes and needs as well as considering the health aspects is much essential.

Introduction

There is no gainsaying in the fact that restaurant business is thriving and unlike before now the trend for eating out and on the go is increasing.

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On the go we can see many eateries on every corner of the street in city but maximum number of the population like to have healthier and hygienic food on an average. Wherein turn there seems to be a lot of confusion concerning healthy eating, and consumers increasingly want to know the nutritional information of the food they eat so that they are acquainted of their health. Most of the people suffering from diseases (like BP, Diabetes) when eating from restaurants cross the constraint of the actual amount and what food they must be consume concerning their health. Hence a system that takes into the consideration regarding the customers interests as well as their health by providing variety and options for personalization comes into account which is healthy food recommendation system (HFRS).What’s important is that people should know what they’re eating, so they can make informed decisions. In this paper we are going to use the concept of recommender system and is based on considering the users’ preferences as well as their nutritional needs. Recommendation System improves with the use of machine learning and create a much better process for customer satisfaction and retention.

Literature Review

The process of literature survey started by searching and analyzing the research papers related to healthy or nutritional food recommendation system. A lots of research has been done on healthy food recommendation system. Some of them are quite related to our system which of them are as follows.

1. DIETOS application is a recommendation system for diet monitoring and personalize food suggestion. The application aim at only providing diet related suggestion.
2. The Food Clustering Analysis for Personalize Food Replacement, provided the balance diet for diabetic patients whereas others health problem are not considered.

Machine Learning – ML is one of the branch of Artificial Intelligence. It is a concept which allows the system to predict the results without any explicit instructions with the help of previously provided examples and experience. In short there is a need to feed data to the algorithm and the machine will built the logic and yield the output.

ML is further categorized into two types- supervised and unsupervised learning.

Unsupervised Learning: Here, the dataset is unlabeled and the system itself needs to predict the outcomes by discovering new patterns and similarities.

Supervised Learning: In this case the labeled dataset is guided by a supervisor as a result of which the model is capable of predicting the outcome when new data is encountered.

In the mentioned system we are going to use above concepts.

Proposed Work

The system consist of the Android application and processing units at the backend.

The Android application is developed using Android Studio, Node.js is used for writing the API’s which are used for communication between application and server, Python language is used for Machine Learning algorithms for the recommendation purpose.

The proposed system takes input as user’s previous history and a set of health related questionnaire, and provides a list of food items which satisfies user expectations and which can be considered for further recommendation.

The main components of the system are:

• User module:

The user on opening the app would be directed to the login/register page (in case of new user). On registering, the user would be asked few details like name, age, weight, any specific health issues (optional) like if the user has any chronic illness e.g. BP, Diabetes, allergies etc. along with some specific details.

• Homepage module:

1. Once the user has created the profile, the homepage is loaded. It contains the search bar where the user could type his/her choice. On entering the page displays the searches relevant to the query types. It is possible to apply filters as well as sort the produced results.
2. The homepage also displays the preferred food items automatically according to the time i.e. morning, noon, evening or night.
3. We also have the homepage recommending top n food items.
4. And according to the preferences set earlier by the user, the system suggests the user in having the food items displayed on the page according to the constraints specified.

• Food content module:

On clicking a particular eatery, the price along with the nutritional content of the food is specified.

• Other recommendation module:

The user is also been suggested the food according to previous history. Also for the user A whose interests matches or is similar to other user interests is recommended the food items bought by user B. The user is also been recommended the food items based on common ingredients.

• Nutrition module:

The nutritional value of a food item is calculated based on the user input like no. of servings, quantity etc.

• Ordering and Payment:

Once selected for buying, the food item can be ordered and proceeded with the payment.

Conclusion and Future Scope

The key goal of every business organization should be to ensure life quality along with the motive of increasing business value profits. The new ideas in the market will always be embraced that assures daily needs and preferences. Hence a system that matches the users’ tastes as well providing them with the nutritional information is always preferred.

The recommendation system allows users to have the option of viewing the top-n recommendations, food items based on user’s previous history, and food items based on ingredient-similarity as well as other similar users’ interests. In short the HFRS allows a personalized experience for the user in making an accurate decision according to his/her need and health.

The system can be integrated with the existing applications to recommend food and also give suggestion to user based on their health and nutri