Food Recommendation Systems Based On Content-based and Collaborative Filtering Techniques
Reetu Singh, Pragya Dwivedi
Abstract
On the internet, numerous options are available for a specific type of product. It is tough to manually go through every product in a particular type when a user is trying to choose the best one. Because of this, manual searching is not very efficient. The recommendation system is crucial in recommending the best product in that situation. A food recommendation system is developed in this research paper using K-nearest neighbor’s methods. The food data set is taken from Kaggle. We used Python programming language to implement the system. Our proposed recommendation system recommends food based on food name, food id, cuisine type, diet type like veg or non-veg in the case of content based filtering recommendation. For recommending the food with help of collaborative filtering we have used user id, food id and rating as an attributes.