Food Recommendation System using One-Stage Algorithm

Abstract

Abstract: There are a number of reasons why it's helpful when people suggest their favourite foods. There has been a rise in the use of automated systems that provide recommendations for various forms of food, including recipes, restaurants, and grocery stores. Scientists have been looking at these sorts of systems for a long time, and their findings show they may help individuals not just identify food they would want to eat, but also fuel themselves more properly. This article summarises the current status of so-called food recommender systems, focusing on both foundational and cutting-edge approaches to the issue, as well as essential specialisations such food recommendation systems for groups of users or systems that encourage healthy eating. Assisting consumers in finding their ideal meal is a crucial function of food recommender systems. Choosing what to eat is a complicated and multi-faceted process that is impacted by many things, including the ingredients, the look of the dish, the user's own preference on food, and many situations, such as what had been eaten at the previous meal. This paper formalises the meal suggestion issue as forecasting user preference on recipes using three essential factors: the user's (and other users') history, the recipe's components, and the recipe's descriptive picture. In order to solve this difficult issue, we design a special neural network. The recommender creates a model of eating habits based on responses to previous surveys. The model is then used to prompt respondents, based on the meals they have previously picked, to submit any linked items they may have forgotten to mention. Model-generated cues were evaluated against those that were manually programmed by dietitians.