Food Dicted: A Restaurant & Food Recommendation System

Abstract

To provide an answer for the mundane questions like where to eat? And what to eat? We are developing
an Android Application that uses simple Machine Learning Algorithms like Content based filtering and
Clustering. The first phase of the output consists of prediction of restaurants based on user input and
the second phase includes prediction of dishes based on the tags selected by the user.Now days we are
provided with a large number of choices which is overwhelming,here there is a need to filter and
efficiently deliver information in order to minimize the problems of information overload.
Recommender systems are used to solve this problem by searching through this information and predict
an output according to the users personal preferences. This system explores various characteristics
and the potential of different techniques of prediction to analyze the result.The system uses content
based recommendation techniques for producing food recommendations.It is based on similarity score
of foods. Basically, our system constructs user profiles from the inputs (preferences) given by the user
and food profiles(tags selected) from the ingredients of the food, then it recommends the most
appropriate dish according to the preferences of the users