Implementation of a personalized and healthy meal recommender system in aid to achieve user fitness goals

Recent research implies that people's urge to stay healthy and fit has drastically improved and currently, many people are in need to maintain their physical fitness incorporating healthy food habits into their lives amidst hectic urban lifestyles. Thus, nutrition applications are mushrooming in the fitness domain to aid people to improve their dietary intake, track weight-related elements, and generate meal plans. Considering the applications that are typically built for meal planning, it was apparent that personalized nutrition incorporated with healthy meal suggestions is not well addressed, and hence the need for a personalized meal recommendation system that assists the users to achieve their fitness goals is identified. Learning users' food preferences and delivering food recommendations that plead to their taste and satisfy nutritional guidelines are challenging. Due to the lack of access to a proper meal planning application or without professional help most users follow ineffective, generic meal plans which hinder them from achieving their fitness goals and often cause long-term and short-term health complications. The proposed implementation aims to bridge the gap between the existing meal planning applications and the potential need for a personalized healthy meal plan. This paper succinctly presents the design and implementation of the proposed personalized and healthy meal recommendation system and further discusses the architecture and the evaluation of the design solution.