Abstract
Understanding user Taste Preferences for Food Recommendation - written by A. Naresh , B. Purushottam Yadav , M. S. S. Shaastry published on 2020/06/05 download full article with reference data and citations
Highlights
Data Collection To collect data we have developed a simple chatbot that collects the user taste preferences from his previously ordered food items
User Food Recommendation System is nowadays becoming very popular because of abundant choice food items for the customer and this made the users' choice of choosing a food item complex, among such a vast variety of food items. This Food Recommendation System has predicted food items according to user taste preferences using taste values of every food item as shown in the TABLE I
This food recommendation system, is built intelligently by considering all the factors into account and is helpful for the users to come over the chaos of huge choice of food items
Summary
Abstract:- Machine learning has led to many technological innovations in several industries like marketing, eCommerce, etc...The benefits of AI and ML systems are experienced by many people without even knowing it, in search and recommendation systems as in google search and the way Netflix recommends movies. ML models can hypothetically be trained to understand the taste and predict taste preferences accurately under specific conditions This ML model is to understand the taste and recommend food products based on users’ tastes. For a machine learning model to predict accurately a user’s preference or taste, for food is likely to appeal to people, the machine learning model would need some quantifiable data related to food items [1]. Since this data would need to be gathered in the physical world, it will be quite difficult, but not impossible. · Chemical compounds found in food items · Relative amount(parts per milliliter) of various compounds in the food items · The color of food · The type of food
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